Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models

There are few things more complicated in analytics (all analytics, big data and huge data!) than multi-channel attribution modeling.

We have fought valiant battles, paid expensive consultants, purchased a crazy amount of software, and achieved an implementation high that is quickly, followed by a " gosh darn it where is my return on investment from all this?" low.

A lot of that is because of all the stuff we don't know. There is lots of missing data. And as if that were not enough, there is lots of unknowable data. Neither of which has stopped Gurus and Masters and Agency High Priests from trumpeting here's the next thing directly from Lord Krishna that will solve all your problems.

So, let's apply Occam's Razor to this complicated challenge. Let's try to make some sense of it all.

By the time you are done with this post you'll have complete knowledge of what's ugly and bad when it comes to attribution modeling. You'll know how to use the good model, even if it is far from perfect. I'll close with a custom attribution model into which you can insert all your biases – sorry, I mean expertise – and get something better than good to make incremental progress from where you are today.

My macro goal is to make you dangerously informed. By the end of this post, if you pay attention, you'll know the often hidden nuances and you'll be dangerous to any analyst/consultant/vendor who walks into your cubicle/office with I've got the God's-gift-to-humanity, easy-to-implement solution with insights riding out to you on a Unicorn.

In this post we are going to take a close look at MCA-ADC. Multi-channel attribution across digital channels. Looking at the picture above … we've spent money on Social, Direct, Search, and Referral efforts and received 767 conversions. But how do we distribute credit for the conversions across all those channels?

It is a pretty easy question to answer. I normally ask people to look at the Path Length report in the Multi-Channel Funnels standard report in Google Analytics (or equivalent tool if you are using SiteCatalyst or WebTrends or other web analytics tools).

If a significant percent of your conversions have a greater than one path length, you have an attribution problem. Combine that with the excellent multi-channel conversion visualize (in the Overview section) and you have yourself a view of your marketing that will freak you out.

It is also ok to weep a little at this point as you realize the extent to which every single decision you've made about allocating your marketing budget is awful. Weep a little for that inconsiderate "friend," last-click attribution.

[One of my favorite parts of this Venn -diagram is the implications on organization structure. Some CxOs see it immediately, other times I have to walk the horse to the water and force it to drink. The outcome in either scenario is a restructuring of the organization that is exquisitely geared towards taking advantage of portfolio optimization. Related implications of what you want to do in-house vs. out source to an Agency. Really fun stuff, really long- term strategic implications. From a Venn -diagram. Who would have thunk?]

The Best Next Steps/The First Best Steps.

The simplest way to start is to look at your Assisted Conversions report in Google Analytics. Look at the last column: Assisted/Last Click or Direct Conversions.

• If you see a value less than one, that channel has a higher tendency to drive last click conversions. Hurray, hurray!

• If you see a value greater than one, that channel has a propensity to be present earlier in the conversion cycle. These channels are getting zero credit in last click attribution platforms (read that as: all standard reports in all web analytics tools). O. U. C. H.

At this point you should educate your management team on this specificity. "Look we might not be valuing all the performance we get from our marketing channels. Here are the specific channels that we are undervaluing." (Where the ratio is greater than one.)

You can even use that column to adjust some of the budget allocation right now, without any attribution modeling, and measure the outcome. It is imperfect, but it is such a simple first step.

It is likely your CxO will want you to explain which channel comes first ("introduces our brand to the customer"), which channel comes second ("nurtures our potential customer"), which channel comes fourth, fifth … and last.

You can use the Top Conversion Paths report.

It is very important to point out that this is a completely foolish exercise to undertake. For the same reasons that path analysis is a waste of time. There are too many paths, and you can't actually control the path that a potential customer can take. Even if, and this is not possible, I said to you that the path is Direct, Social, PPC, Organic, Referral for 5% of the site traffic … what would you do? It is not possible to force people down that path!

But show the actual report. Let them arrive at the obvious conclusion. Be a hero. :)

The next question will be, what are the best ways for us to allocate credit to all our marketing channels properly?

I'm glad you asked, Ms. Executive.

Multi-Channel Attribution Models.

There is a free tool inside Google Analytics called Model Comparison Tool. It is sweet. It allows you to attribute credit to all your digital marketing channels involved in conversions (macro and micro conversions). You can visualize the impact of applying three models at one time.

For example, what if we used a linear attribution model instead of last click?

OMG! OMG! OMG! So cool!

All I have to do is look at the very last column and look at the green and red arrows and get guidance about how I should shift my budgets?Yes!

OMG! Really?Yes.

And you are telling me that the Cost Per Acquisition for my display campaigns is not $201 but rather a lowly $155?Yes.

Get. Out. Of. Here! That is so cool. Finally my amazing blinking hit the monkey display ads are getting all the credit they deserve!

Time to burst your bubble just a little.

The tool is actually that good. Apply the right model and you will not only distribute conversions across multiple touch points, but you can also look at the impact on the CPA (this really is OMG, I peed in my pants a little cool). You can even get great first-step guidance about how to rebalance your portfolio from that last column.

But the weakest link in the chain is the attribution model you use. The recommendations you get are only as good as the model you use.

With that in mind, let's look at the standard models available inside Google Analytics (and some of the high-end analytics or attribution analysis tools).

Just so we have a visual guide through this learning process, let's use the above image as a reference. Look up, memorize the steps to conversion. Ready?

1. Last Interaction/Last Click Attribution model.

This is the standard attribution model in all web analytics tools. It is applied to all the standard reports you see.

[The only exception to this rule is Google Analytics which, and I deeply passionately hate this, applies the #2 model below in all its standard reports.]

You can see why this model is silly. If 767 people converted as a result of the above experience, saying that all the credit should go to the Direct channel is silly. [Bonus: Learn more about what direct traffic is: Make Love To Your Direct Traffic.]

Social, Organic and Referral were also involved. We should figure out some way to identify their contribution to the conversion process, because they were involved in some form.

Historically, all tools used last click attribution because the one thing they could confidently say is what drove the converting visit. And they did not have the technical horsepower to do Visitor-centric analysis. Both these problems are solved now.

The only use for last click attribution now is to get you fired. Avoid it.

2. Last Non-Direct Click Attribution Model.

Google Analytics is bipolar.

All standard reports in Google Analytics give 100% of conversion credit to the last "campaign" prior to the conversion. Campaign is defined as anything but Direct traffic. So, the campaign could be Social, Organic Search, Email, Display, Affiliate, Referring Site … anything really.

This deliberately understates the Direct visits that lead to a conversion. In our picture below this model would say all credit goes to Referral.

This is imprecise. Why give credit to a campaign if it took me another visit where I remembered your URL and typed it in and came to your site? Why should the visit where, say, I saw a great promo or you recommended something based on my prior visit not get some credit for the conversion?

I believe this is a mistake. A historical legacy, perhaps. It should be courageously fixed.

Bonus: This model is also the irritating reason why none of your standard Google Analytics reports match your standard Multi-Channel Funnels reports, even if you look at conversions in the standard MCF Overview or Assisted Conversions reports.

3. Last AdWords Click Attribution Model.

My words for this model might get a little bit vitriolic, so I'm going to keep my mouth shut.

And to think you never thought that was possible. : )

This model is profoundly value-deficient. There. I can be nice.

4. First Interaction/First Click Attribution Model.

Reverse of last click. Rather than giving all the credit to the last click, give all the credit to the first click.

In our example above, switch 100% of the credit from Direct to Social.

This is a gigante mistake.

First click attribution is akin to giving my first girlfriend 100% of the credit for me marrying my wife.

Makes no sense, right?

If the first was so awesome, how come I needed #2, #3… to get to the most perfect person – I mean, campaign :) – for me?

With last click attribution there is at least some certainty that something about that campaign, something about that visit to the site, resulted in a conversion. With first click you just have faith. Or a HiPPOs (Highest Paid Person's Opinion) fervent "gut-feel."

Eschew irrationality.

5. Linear Attribution Model.

This is less wrong.

That's it. Just less wrong. Use it if you are shooting for that.

When my son was smaller he would go to competitions (sports or IQ) and everyone would get a participation certificate.

Life, it turns out, is not utopian. When there is a competition, someone gets a gold medal, someone gets a silver, and someone gets a bronze. Everyone else goes home a loser, motivated to work harder the next time and win.

You should not treat your marketing optimization program with the same level of outcome optimization that is applied to five-year-olds. You can, and should, do better.

If someone threatens your life, use this model. Give everyone who contributed a participation certificate. But if you are not in a life-threatening situation, other models might help you actually understand which channels are contributing more value and which are not. And two of those models are just one click away.

6. Time Decay Attribution Model.

Ohh …. much better!

The core premise of the time decay model is this: The media touch point closest to conversion gets most of the credit, and the touch point prior to that will get less credit based on a smart and simple algorithm.

You only have to think about it for five seconds to realize it passes the ultimate test for everything: Common sense.

We could argue about how much credit the last few should get and how much the rest and how much the first. (Or we could not.) But overall it does seem to make sense that the further back a media touch point is (Organic Search and Social in our example) the less credit it should get. After all, if the touch points were magnificent, why did they not convert?

One of the cool things about this model is that you can customize the half-life of decay and insert your own feelings into the attribution process. Notice I said feelings. :)

If you are going to start doing attribution modeling, the time decay model is a great, passes the common sense test, way to dip your toes. Go to the Model Comparison Tool, click on Select Model, choose Time Decay, and let thoughts be provoked!

Bonus: Adjust days prior to conversion on top of the tool based on your Time Lag report in the Multi-Channel Funnels folder.

7. Position Based Attribution Model.

In some ways I really like the position based model because I have opinions – sorry, I meant to say expertise :) – and it is so easy to insert those opinions into this model and do some cool stuff.

That is what makes it a dangerous first model to use. If you don't know what you are doing, it is GIGO very quickly.

By default, the Position Based model attributes 40% of the credit to the first and the last interaction and the remaining 20% is distributed evenly to all the interactions in the middle.

1. See my perspective on first click attribution model above. 2. Understand why I believe that as designed the default position based model is sub-optimal. 3. Promise me you won't ever use the default one. 4. Feel really great you dodged a bullet.

Of the six attribution models available, there is one that you can use with little thought and still get value (Time Decay). One is not great, but won't completely kill you (Position). Three are so weak that you should not acknowledge them if they pass you in the street (and actively warn your friends to avoid them!).

Why are there so many models? The known world is smaller than the unknown world. There are always corner cases, there are always weird scenarios, there is always someone who wants to do something odd. All these reasons are good reasons for all these models to exist. But do go into using any model with open eyes.

There is one more thing you can do after you are done with the first step, playing with and experimenting with the results of the Time Decay model. You can create a customized attribution model.

8. Customized/Personalized Attribution Model.

(I've said this twice already but let me say it again, don't go into this until you play with the Time Decay model and have spent a good few weeks learning the implications and trying to take some action. It is a very good learning experience.)

I love using the customized attribution model, and I'm grateful that the team at Google made it free for everyone rather than having it only for Google Analytics Premium.

With the custom modeling tool you can use the Linear, First, Last, Time Decay and Position Based models as your starting point, and then layer in other factors you consider to be important for your business to create your own attribution model.

I spend a lot of time with the business leaders, marketers, understanding historical performance, current media-mix and spend patterns before I create a customized model for them. Among the questions I ask the leaders are:

+ What type of user behavior do you value?

+ Is there an optimal conversion window you are solving for?

+ What does the repeat purchase behavior look like historically?

+ Are there any micro-conversions defined with engagement type goals, tied to the economic value?

So on, and so forth. These provide important context in making the decisions that will go into a custom attribution model.

From my portfolio of custom models, let me share one that has often served as a starting point for many customers.

Setting aside all humility for a nanosecond, I call it the Market Motive Mindblowing Model!

Click on Select Model in the Model Comparison Tool. At the bottom of the drop-down you'll see Create new custom model, click it.

Step 1: Select the baseline model.

I start with the Position Based. Then specify the amount of conversion credit based on the position. Here's what I use…

If you've read this post carefully to this point, this distribution of credit should not come as a surprise to you. From all my experimentation I've found that taking out the last channel (whichever one it is) causes a material impact on the conversion process, so it gets a "good amount of credit." The middle channels have an important role in driving people to the last interaction, they are recognized for that. The first interaction deserves some credit for the conversion, but not as much as the middle or last – for obvious reasons.

My distribution above is a good starting point. It is also really easy for you, as I often do myself, to experiment with different distributions, note the impact and optimize.

Step 2: Select the lookback window.

My process for picking the optimal time period to look for campaigns/interactions/media touch points to distribute credit over is to use the Time Lag report in the Multi-channel Funnels folder. It gives you the distribution of typical behavior.

My rule for picking the lookback window is to pick "close to the upper limit of the number of days to conversion, excluding the outliers, plus a bit more."

In this case it was a B2B client, long conversion cycle that lasted around 65 days, ignoring the outliers, so I picked 75. Just to be conservative.

Look at your own Time Lag report, come up with your own number. I'm a big believer in not going back to every single campaign, no matter how far back, and dragging it in to give it credit. If it was so awesome, it would have kicked off a conversion cycle for us that falls within the upper limits indicated in the Time Lag report.

The next two steps are critical. They are both really cool. But more than that, they help us wash away some of the sub-optimal decisions we might have made in the above two steps. Pay attention.

Step 3: Select the engagement based credit option.

We now go in and apply a rather clever rule to adjust credit for our campaign based on the behavior of the user that came to our site. This is particularly important for the touch points prior to last click.

Time on Site is always a tricky computation. In all Web Analytics tools, unless you apply custom code, time on site is not computed for bounce visits or for the last page viewed in a visit.

Hence, I prefer to use Page Depth as a proxy for site engagement.

In this step we are telling GA to give more credit to campaigns that deliver users that have a higher engagement with the site. So if a user from campaign X see five pages during the visit on my automotive website and campaign Y sends a user that bounces, campaign X will get more credit.

Only seems fair. And now you can see how some of your credit distributions in step one will be auto-corrected based on the type of engagement campaigns deliver.

Step 4: Apply custom credit rules.

The last bit of mind-exploding fun. We are going to select some custom rules that apply uniquely to our company (remember the five business questions above?).

You can literally apply any custom rule you want. You can go in and say "for all bounced visits from rich media display campaigns give the campaign 2x the credit." You would not do that, but you can. You can do the reverse, "give every campaign with Bounced Visits zero times the credit of other interactions in the conversion path."

I take a simpler first step. I want to value my campaigns based on the interaction they deliver. If there is only an impression (people only see the ad), I value that a lot less than ads that get people to click on them.

To do that first I choose Interaction Type. Then I choose Click from the Exactly Matching drop down.

Finally, I would like to have ads that get clicks to be extra rewarded and, in this case, get 1.4 times the credit of other campaigns in the conversion paths (in comparison to ads that just get impressions).

Why 1.4? After some experimentation, that was determined to be the optimal amount of value for this business (remember the custom model questions above?). There is no way out, you have to experiment.

That's our last step.

Other ideas for this last step include the ability to give generic or brand keywords more or less credit. Or giving Direct or Social more or less credit. Or giving all Social visits that are the last click prior to conversion only half the credit compared to other interactions in the path (Include Position in Path Exactly Matching Last and Include Source Exactly Matching Social, where Social is your campaign tracking parameter).

Totally your call. Just remember to drag your common sense along when you sit down to do this.

[sidebar] Once again in step four you see how clever use of custom filters can auto-correct some of your earlier assumptions related to distributions of credit in step one. If campaigns in the middle, or the first one, don't have the optimal interaction they will automatically be penalized. [/sidebar]

That is all it takes, four simple steps, a pinch of understanding your business and a sprinkling of common sense.

It should be completely obvious to you that this model is based on a specific client's business environment, my experience, and business priorities. While I believe it will serve as a good starting point for your very own custom attribution model, it might not be optimal for you.

Hence, more than anything else, I would love for you to follow the thought process and the reasons for making choice x or choice y. Then apply that level of critical thinking as you go about creating a model for your digital business.

Multi-Channel Attribution Analysis.

Once you have your models sorted out, I recommend you get rid of the last click attribution model. It only ends up being a heavy useless anchor on your analysis. If you want to do comparative analysis, choose Time Decay for the first one (we know it is better than last click) and choose the Mindblowing Model (or your custom model).

Your view will look something like this.

Focus on that last column, % change in Conversions.

Use the guidance provided (essentially a positive or negative shift away from the reference model, in this case Time Decay) to make recommendations for a different allocation of funds/effort for each marketing channel. Comparing the two models, you can see where your previous model/belief was wrong. Try adjusting your budgets accordingly for better success. As an example, in the above analysis Referrals are performing much better than we would otherwise have credited them for.

For the most optimal outcome for your company follow this 3-step process:

1. Create a hypothesis based on above analysis for how to better allocate budget across marketing channels.

2. Test that hypothesis using a percent of your budget and measure results.

3. Be less wrong over time.

Multi-channel attribution modeling and analysis is not a one-time effort, it is something you'll do all the time. Not every day, but at least do an operational review every two weeks and a strategic review (with recommendation for changes) every month.

In Closing, Five Quick Tips/Reality Checks.

I want to leave with some insights from the front lines of solving the MCA-ADC, MCA-AMS, MCA-O2S challenges. Hopefully these will help you get a jump-start in your own efforts.

#2. One of my favorite exercises is to do the above analysis based on Cost Per Acquisition, rather than just conversions. You may be getting a lot of conversions, but the CPA can kill you. Notice above I only have two CPA values. For the rest I need to upload cost data into GA for my Social, Referral, Organic Search (yes, it costs money), and Email campaigns. You do too.

#3.You don't have to do attribution analysis for all your conversions in aggregate. On top of the attribution Model Comparison Tool, you'll see a drop down under the word Conversion. Click. Choose any conversion you consider to be important. You can do attribution modeling uniquely and optimize your marketing efforts just for an ecommerce transaction. Or you can do it for email subscription signups, or downloads, or videos played or anything else you consider to be important.

#4. Remember all of the above just covers Multi-Channel Analysis-All Digital Channels (MCA-ADC). There are two other, even more complex, attribution analysis scenarios: MCA-O2S and MCA-AMS. You can learn more about them here: Three Types of Multi-Channel Attribution Problems.

Don't be disheartened that all this complexity exists. Take things one step at a time. Standard Time Decay model first. Then your own Mindblowing Custom Model. Then Experimentation. Then MCA-O2S. Then MCA-AMS (it is so ironic this is harder than O2S!). With every step, you are making your company smarter. Less wrong every day.

Optimize for your online media-mix at the start, then move to optimizing your online and offline media-mix. Media-mix modeling is harder and more time-consuming (hence the $10 million bar), but the payoff is huge and can be a competitive advantage.

We are done! Attribution modeling mastered! Hurray!!

: )

As always, it's your turn now.

Are you doing any attribution modeling at the moment? What frustrates you about it? What benefits have come from your credit re-allocation efforts? Run into any organizational/ego problems with senior leaders yet? Love First Click or Linear attribution, what am I missing in my thinking? Which model is your BFF? What are two fatally flawed choices in my Mindblowing Model? What would you do differently? Has it been easy to go from analysis, end of this post, to insights to action?

Tyson: Does the company not want existing customers to buy more, or find the company when existing customers are looking for them?

And how do they know for a fact that a brand search is by someone who is an existing customer? Have they validated conversions via brand searches and found that 100% of the orders were from existing customers?

I suspect the answer to both is no.

I would run an experiment. Take some campaigns, don't have any brand keywords in the portfolio (be it just a keyword portfolio, or a search, email, social, display etc portfolio) and see what happens to the conversions. Then compare that to one with brand keywords. Now you know the value. :)

-Avinash.
PS: In case I was not obvious, discounting brand keywords in the absence of the above experiments is flawed.

Great post, and now I am really glad I didn't try to write my own first :)

Seriously this is a great description of how to think about attribution models and reporting but, and it is a big but, really what is the policy that this analysis is going to drive?

Ostensibly it is about budget allocation, but since we followed a fixed policy when collecting this data, we really have no information around how our KPIs might increase/decrease as we reallocate based on any one of the distributions suggested by the various attribution models. I know you can say it is a starting point, but, and I am not trying to be pedantic, I am really not so sure. To me by advocating following this formalism, we might be implying that this analysis answers something that it really doesn't.

The attribution model as is, tells us nothing about how it will respond to change – which is exactly what we are looking for.
In order for this stuff to work, at each point in the process were we might try to affect behavior, we need to embed at least a null option, so that we are working with a decision process, rather than just a chain, no?

For #1, attribution modeling incrementally provides better answers, it helps you answer "if everyone who pitched in got some credit, how would the conversions look like?" (And being pedantic, I don't think of this as analysis, I just see it as modeling. Subtle but important difference. :)).

For #2, you can and should have a practice inside your company to do controlled experimentation. Then you can have your null option, delight will follow! :)

Quick question – The selection of the number of days for the duration of the lookback makes intuitive sense. However, for the position based ratio selection – how would one evaluate whether a 10-50-40 is more relevant than say, a 40-40-20 model?

In the absence of a control group, what is the best way to arrive at the 'right' weights?

Neelakash: There is no blessed distribution, the only way to arrive at it is to use experimentation. Take credit distribution A, B, C (A can be control) and run a test to measure results. Go from there.

As mentioned in the article, you don't have to wait for a perfect answer to start using attribution modeling. Anything you do will be better than last-click. The question then is how much better can you be. :)

I've been following your blog from past six months now and I make sure to visit at least once a week on your site. The learning from your posts have helped me with enhanced understanding of digital ecosystem.

Having worked Hands On with these kind of problems, I can certainly say that algorithm based multi channel attribution is where the marketers will have to move eventually to measure the channel performances.

Avinash – I feel like web analytics was complex, and it's just gone to another level altogether with new features such as attribution, cost data, and the entire Universal Analytics universe. There's no better time to focus on these tools and strategies — before the learning curve is simply too steep!

I have a sad attribution story to tell you. An affiliate partner of mine has traditionally focused on a last-touch model for paying out their sales. I dedicated my entire content strategy to focus on the advocacy of their product (as did many of their other affiliates), creating a rich community surrounding the brand in the process.

Recently they shifted to paying out on a first-touch attribution model because they wanted awareness, not advocacy. Now, part of me can't blame them because they really do have a great product and, to a certain degree, they don't need me to tell people how great it is.

But on the other hand, this shift in how they compensate their affiliate partners has seen the advocacy for their product dry up nearly overnight from these hubs in the community. Their shift to partners who create awareness has fundamentally changed the dynamic of the brand, which is just one more thing to consider when looking at attribution.

I will lean particularly heavily on this post for my future attribution activities. Thanks much for such specific thoughts and screenshots on this advanced topic.

Josh: What a great story. Sorry, I mean what a bad story for you that illustrates the challenge so clearly for everyone! :)

When people choose first click, as your client, what they are saying is:

"I don't care about the outcome for my business, all I care about is that people know about my product. If then they decide not to buy it, if all of that knowing was a complete waste of time, that is absolutely ok. And PS: Don't tell me if it was a waste of time. PPS: LA, LA, LA, LA, I can't hear you, LA, LA, LAAAA!"

:)

It sounds astoundingly crazy. But that is exactly what they are deciding to do.

There is no way to know if First Click could have delivered the conversion all by itself. If it could have, why do you need all of the other advertising/marketing?

Life is not about a OR, it is about an AND.

Last click OR first click is silly.

First Click AND "what else caused the conversion" is the right approach.

Then we can, as we do in the post, discuss how to distribute credit for subsequent touch points, we can discuss how to overweight clicks (as I do) or overweight impressions (which I hate, sorry), we can discuss duration etc. All debatable to identify insights to optimize the portfolio of marketing that lead to a success.

But you MUST optimize for the portfolio and not a silo. If you do the latter, you are cutting off your legs to try and run faster. Ok, maybe I'm being a drama queen there, but you get my point.

-Avinash.
PS: For true Analysts it is easy to figure out the impact of a corrosive decision like first click attribution. Just run a controlled experiment. Cell A: All budget put into first click. Cell B: Budget allocated according the advice above. May the best Cell win!

Avinash: First of all I want to tell you that I found this Article very informative.

In regards to Josh´s comment I have to say that I understand why the Affiliate Partner might act this way.

In the example of an Affiliate Partner, like the one above, we might be talking about a voucher affiliate. Voucher affiliates, who provide discount vouchers for various online shops, basically feed off the shops Brand keywords and are often visited once the customer has already reached the basket. The customer sees that there is an option to enter a voucher code and starts googling.

This is why I would imagine the Affiliate Partner might want to decide to compensate Affiliates (or just some Affiliates) for reach (First touch). Otherwise we might be giving credit to a channel that doesn´t necessarily drive conversion but merely steals margin at the end of the funnel.

From my perspective an attribution model based on channel mix and position would be justified. Have you tried something like that?

Dirk: The way attribution modeling works in Google Analytics, or the other lovely tools, is that in the scenario you describe the Affiliate Partner will appear as the media touch point just prior to conversion, and not the first one.

The first one will be the channel that drove the person to the site (search, social, email, display, etc.). Then during checkout the person saw the coupon/discount code box and went off to Google/Bing.

This is the reason for my original guidance. You simply can't guess or "stack the system in favor of x or y." Just create the cleanest set of common-sense rules and then let the chips land where they might.

You are ABSOLUTELY right that it is a margin stealing outcome because 1. we have to now give a discount to a customer and 2. we have to give a bounty to the affiliate for "delivering the conversion" and 3. for the customers we get in this manner (price sensitive, discount seeker) repeat purchases might be non-existent.
Avinash.

Avinash, I love your perspective on digital attribution. I do have a couple of nits to pick, though.

You indicated Time Decay is must better than Last Click…not necessarily of course. For a particular dataset/client, using Time Decay may actually optimize away from your most efficient spend. For instance, what if the "true" weight of the last click should be 85% instead of 100%? Using Time Decay would actually hurt you; Last Click would be the better option in this case.

Also, I think your "Mindblowing Model" is just as guilty as relying on opinions/biases as Last Click, First Click, etc. Why choose the 10%/50%/40% distribution? For this particular dataset/client maybe the "truth" is 25%/40%/35%? Or maybe it's 5%/10%/85%? You see my point…deciding in advance what your distribution is allows you arrive at whatever conclusion you wish. In your example, if I decide up front I want referrals to perform well, I can use your distribution. If I want them to perform poorly, I could choose a different distribution.

For this reason, I'm a strong believer in custom, data-driven attribution (statistical) models that let the data decide the optimial weights. You may argue that most digital advertisers don't need something this complex, though I'd argue 1) they do :) and 2) it's not really complex if you partner with the right experts. Otherwise, digital marketers will continue relying completely on their biases and will continue to spend dollars inefficiently.

Dave: Thank you for the nits you have picked. Debate pushes critical thinking, I so appreciate that.

For the first part, perhaps I can restate the point. Time Decay causes less harm in all scenarios than Last Click. Simply because with the latter you ignore 100% of the prior ad/marketing touch points, with the former you at least give them some value. It might not be the right value, but some value.

Once you give some value, we can ask the questions you correctly do in your comment and we can get better.

On your second part… OMG totally! I do hope you did a Ctrl + F and saw how many times I used the word bias. :)

Think of my custom model as *my* starting point from *my* 150 experiences of doing this. Those experiences are unique and most definitely bias my view. But I hope it serves as a starting point for you, if you don't have one.

Ultimately the proof of the pudding is once you take the learnings, experiment with the new budget and realize how awful your assumptions were or how awesome they were.

On the third part… I ever so passionately disagree. As I mentioned even Google Analytics has a data-driven attribution model. It is pretty nice. But it would be silly to suggest that it is not biased and does not suffer from missing variables, missing data points, and remember humans code these models (is there any statistical model that does not include, to a greater or less extent, the bias of the creator?).

That does not make data driven models any less valuable, or in some cases better than the standard ones or biased custom ones. By default with people who have data driven models, it becomes my reference model (you'll see I use Time Decay above as a reference). But math and code do not make unbiased models. (Do not share this with anyone working on google.com!)

Ultimately the only true way to wash out biases (mine, yours, smart algorithms') is to use controlled experiments and approaches like media mix modeling. It is not accessible to all companies (hence my $10 million bar), but when we can do that the ROI is magnificent.

I love your post. I've been deep diving into attribution modeling using the GA tool, and I feel this is absolutely huge for the industry. Most of my clients can't afford to pay the mega-bucks otherwise required, and I was actually custom coding some log based analysis to get some insight for some clients with convoluted purchase cycles.

I am concerned with a similar viewpoint to Dave's above – how do we get rid of our own biases and systematically build a more accurate model. Ultimately, my preference for a model vs. your preference for a model is inefficient. We'd like to understand how our customer functions, and what monetary impact each channel / visit / ad impression has had on each sale.

I love the fact that you've given a default custom model. I'm going to add in a deduction for any remarketing and brand campaigns, as those by default had a different channel that interacted with them first. (e.g. .9x of the regular). I would also channel group them separately.

I've really enjoyed the post and comments. As far as testing, I've been thinking that the best way to test a hypothesis would be to segment (say) all of our US traffic in half (e.g. take all the states, look at current performance (e.g. conversion rates and AOV for an ecommerce site), and make sure I have 2 equal segments.

I could then optimize all of my campaigns on one segment with one attribution model in Google analytics, and I could optimize the second segment with my original model. At the end of the testing period, I could then look at overall conversion rates. This could also give me insight into how well solving MCA-ADC correlates with MCA-OMS.

While this is a pain, it's quite doable.

The challenge I face with attribution modeling tool is getting insight into where I am off-base on the attribution model. Sure, we can make assumptions, but ultimately, I'd like to test those assumptions.

I know you have an article about the process of conducting a real test somewhere on the blog, but I can't seem to find it.

Thanks for your reply and for the neat info graphic. I do support your take on a data driven approach-coming from a statistical background myself. I have one question:

1. The infographic seems like a variation of a Media Mix Model. Data issues aside, one of the limitations of a Media Mix Model (in my opinion, at least) is that it can not go deep enough into the specifics of advertising elements. MMM capture the essence of drivers into the response variables. However, when you consider a channel like Search with varying geotargets, campaigns, keywords, etc., the combinations of these factors can run into thousands. I am curious on the trade-offs between data granularity and model robustness when you are building the attribution model.

Regardless, I think the data-driven approach is a step in the right direction. Thank you for sharing.

Effie: I'm sorry I'm not sure which image you were referring to. But in context of media mix modeling (and I'm a big fan), you are right that initially when you start off you are simply trying to get the mix in your overall big buckets right. For example, how do we distribute our budget between Paid Search, Display, and Paid Social. Or between TV, Radio, Search, YouTube.

But that is not the end of the road. You can use media mix modeling to tease out the distribution between Brand Keywords, Category Keywords, YouTube Search. Etc. Etc. We have to find the balance between how much fiscal sense it makes.

It is important to remember that we lean into using techniques like MMM because other techniques don't get us the answer. In as much there are always times when we know what to do, but not exactly why we must do it. All of course based on data.

Effie, thanks for the note. At a high level, yes, you could say digital attribution modeling is similar to marketing mix/media mix modeling in the sense that you have marketing phenomenon as indepedent variables and a response metric as dependent variables. The input data is a little different though: digital attribution typically includes 0/1 variables (did someone view an ad or not) and the dependent variable is also 0/1 (did someone make a purchase or not) — think logistical regression. Mix modeling generally is run on aggregate data (sales at a DMA level as a function of marketing activity in that same DMA), and the depedent variable is continuous. From a statistical modeling perspective, the difference in type (0/1 vs. continuous) results in using a different modeling approach (logistic vs. linear/non-linear regression).

Also, I agree with your assessment of mix modeling…it can have its limitations when the goal is to drill down within a specific channel. Having said that, if you obtain the right data, you absolutely can assess the impact of these channel-specific attributes within mix models. For example, you can choose to measure the impact of TV in one variable, or you can create numerous variables for TV based on creative, daypart, etc. The same goes for digital…as long as you have the correct data. Honestly, I think mix modeling should be done in conjunction with a more detailed channel-specific analyis to get at these questions: execute mix modeling to decide how much spending to allocate to digital, then use attribution modeling to determine how the budget is allocated within digital (search vs. display vs. video etc.).

My dream – if at all possible, is all analysts would actually take the time to read and understand this critical aspect of business optimization (note that I intentionally wrote "business", not "web" or "online"). All that's missing is an exam at the end! :)

A great primer to get started on attribution modeling. Especially with Google Analytic's new feature to track GDN impressions, it's up to us to upload cost data and start playing with the different models!

If you want to know the tactical implementation of using GA Attribution modeling tool, you need to not just tag your campaigns but also leverage custom channel grouping. I wrote about how to do that here if you're interested :)

As always, great article – thanks for sharing so much with the community!

Now, if only Adobe SiteCatalyst would get on board and offer similar capabilities in their base product. Until that time, if anyone knows of any work arounds for getting similar data in SiteCatalyst, let me know.

I'm very glad you mentioned that this post is a deep dive into MCA-ADC and *not* MCA-AMS. *Question* –> to what extent does the MCA-AMS + cookie deletion world we live in (incognito, anyone?) make the insights we arrive at by applying a model, even a super-sweet MMMModel, just unreliable. My guess is that your answer will be -> we're "less wrong." Curious if that's what you think and if there is any way for us to quantify the quandary that MCA-AMS causes.

Re: Problem with de-valuing Direct traffic in Last Non-Direct Click.. I hear what you're saying but don't wholeheartedly agree. I'm sure that you too have seen *plenty* of last touch direct traffic simply caused by session timeout. Heck, all the other sites I was on also just GA timed out while I read this post and commented! Personally, I prefer Last Non-Direct Click to Last Touch… (please don't throw rocks). Luckily, the session timeout issue is "somewhat" quantifiable (at least for ecommerce sites) by looking at how many macro conversions have a shopping cart or 1st page of checkout landing page. That data can then be applied to a custom model.

Lastly, hurray for laying it on thick with regards to custom models. It is a good message to hammer home.

Yehoshua: I have to share a philosophy first (I think we both believe in it). It is always possible to find problems in your data, it is always possible to chase the last bunny down that very last, very deep fruitless rabbit hole. One of my biases is that I stop at the point when I reach diminishing margins of return. Because otherwise I could die data puking and never add any value to any business. This is a gigantic bias I have. I wanted to be transparent.

Ok, on to your kind questions…

First party cookie deletion does not have a material impact for most clients in the time-lag to conversion. Most of those fall within the 20-40 day window. The longer you stretch time, the more material the impact.

With MCA-AMS, cookies are valueless. Either you have an opt-in permission driven model to track a human (awesome, go crazy with attribution), or you don't (attribution dies a early death).

Regarding session time-out, luckily it is a easy thing to validate. Look at the percentage of sessions where the start page for the same User is exactly the same as the last page of a prior session. If your data says that happens 50% of the time, or 20% of the time or whatever, use last-non-direct. I've never seen a material number from this analysis and until then I refuse to take on faith what the GA team wants me to believe!

Thanks for your reply. I see that perhaps I didn't express my question as clearly as I hoped to. Let me give it another shot.

I lumped cookie deletion / browsing in "private mode" together with MCA-AMS because ultimately no cookies = no attribution without the opt-in permission model as you mentioned. Not exactly the same thing, but close enough for this issue.

You mentioned your bias regarding stopping at a point where you find diminishing margins of return. That is (somewhat) what I was trying to get out. At what point have you found there to be a diminished margin of return doing MCA-ADC analysis because of an MCA-AMS reality? Do you have thoughts with regards to quantifying that? Do you feel that trying to quantify that is a waste of time?

For example: stop segmenting Organic Traffic by branded / non-branded keywords because (not provided) is at 80%. It makes no sense to try to analyze data you don't have.

My guess is that you believe that being able to quantify issues with data quality leads to better decisions. For example, you mentioned uploading cost data for Organic traffic because, yes, it costs money. If one were to ignore the fact that iOS6 doesn't (didn't) pass referrers, they'd have a skewed sense of the value their getting from their SEO efforts. However, if I can estimate the amount of iPhone traffic that is probably Organic based landing page yadda yadda… you get the point. I'm speaking about using "broad strokes" here; I too agree that chasing small rabbits around all day would be tiring and fruitless.

Alternatively, you may say that all exercises that try to quantify data quality issues are indeed just chasing rabbits.

Back to your reply: MCA-AMS -> either you have opt-in (yay!) or you don't (dead!). Great. But it's not as if sites are either in the AMS camp or the ADC camp. There's a cross-over for sure. Does it matter?

Thanks for this. As always, you've identified problems and provided solutions (or at least where to start). I will point clients to this post in the future when I mention MCA and they stare blankly into the distance.

Avinash – It's great to see someone of your intellect and stature shedding some light in this area of analysis. I see a lot of confusion on this topic, from both newbies and "experts" alike, but not a lot of useful discourse.

Now, the next logical step is helping people understand #2 from your 3-step process at the end:

"2. Test that hypothesis using a percent of your budget and measure results."

Very entertaining read, and valid points, no doubt – as long as the question is: "On the whole, how should I spend my bucks?" But other questions need other attribution models, I think.

When doing a product launch, for example, the "First Interaction" model proved to be very helpful in optimising launch strategies, since it answers a very different – and in this case crucial – question: "How do I get this indispensable first contact?"

Ultimately, attribution modeling is quite possibly not about finding The Truth, but about finding the best set of models to answer different questions, I guess.

Lukas: You are absolutely right, with attribution modeling we are indeed trying to answer the "I have $xx mil in my marketing budget, how should I best distribute my budget amongst the marketing channels to maximize profit for my company."

Avinash: Well, yes, as I said: As long as it's about profit, I completely agree with you. But as soon as other business goals come into play – purely growth-related ones, e.g. – other models have their merits, in my experience. Even First Click models.

One thing that I also feel compelled to mention is this kind of modeling is simply a measurement of the past, not a prediction of the future. So many marketers confuse the two and run themselves in circles because of it. A new dollar invested in a marketing channel will not necessarily perform the same as the existing dollars in that channel. In some instances, its more predictable (increasing bids in paid search) and in some cases its less so (viral campaigns, press releases, new keyword targets, etc). I understand you can only address so much in an article, but this, to me is incredibly important.

The thing I struggle with the most is evaluating the models and advancing my modeling based on accuracy rather than subjectivity. If I identify an area to shift budget to, then do so and receive sub-par results. Was my attribution method the flaw or my execution of the new campaign? Or, perhaps, I receive stunning results, was it a false positive?

The other cautionary piece of advice I would give is never expect an attribution model, no matter how complex, to replace the need for a human being to interpret the results; expect the model to augment that person's decision rather than replace it.

Dave: I could not agree with you more emphatically. That is why I had the couple lines of guidance just before I started my closing thoughts.

Identifying all the variables in play is very important. If the model goes from 90% Search, 10% email to 40% Search, 20% Email, 40% Display, it is very important to be aware of all the new variables in play so that you can make a smarter decision.

This is partly the reason that we all start with attribution modeling, but quickly end up with controlled experiments because it allows us to identify some causality.

Nice – And while complex multi-channel by customer type and segment (informing the channels and better – the interaction point) about the customer/stakeholder changes the assumptions and formula dramatically and then there is the multi-screen…

I built a framework to predict each customer's movement/lack of mevement through an exhaustive contact matrix (CxC matrix: customerworthy.com/cxc-matrix ) following customer path (horizontal) by company/affiliates channels (vertical) which is unbelievably easy to populate – just count the contacts per channel to start – and monetize customers passing/not passing from stage to stage.

This method connects all the pieces and links all the stakeholders in and around a company (agency, affiliates, distributors, call centers, even manufacturers after market)

If you want to build the killer app – use the CxC Matrix to populate a complete simulation (time shift for testing and expose variables per interaction, environment, competitors, seasons – again, easier than it sounds – and visualization socializes experimentation and input for innovation )

Please feel free to add this dimension to your analysis – I'll send you free pdf copy of book Customer Worthy (or $50 @ Amazon) and of course answer any questions about how you might use this – consider CxC matrix open source for customer experience management.

Hi Avinash, great post and something we've been (finally!) talking about a lot at my company.

I see on the "custom credit rules," you can include interaction types = impression. My question is, how does GA know that an impression occurred if the user never visited our site (hence no page-view)? We advertise heavily via display media, both on the Google Display Network as well as other ad networks.

If it is possible to track impressions via GA, it would solve a lot of problems for me. I've been pushing hard within my team for the last eight months to hire our own ad server, so we could have a 360° view of our marketing campaigns (impressions + clicks) plus the on-site activity.

Gretchen: The GA team has recently developed the ability to link unclicked impression data from Youtube / GDN into GA MCF / Attribution. This way, you can see the impressions on the users path to conversion, and you can assign partial value to these impressions.

Besides the complexity for the web analysts to understand en set-up the best model, I think the biggest challenge is to translate this into understandable conclusions/reports for other not-so-analytics-savvy stakeholders in your organisation. If you explain this to other people, you will get a lot of questions and you won't be able to answer them with a simple yes or no (what a lot of people expect of web analytics).

Showing your new and better model, also means that all the things web analytics told us before about channel attribution (on last click), were 'wrong'… So also be prepared for this.

In general this tool is a giant step forward in channel attribution, but I also think it requires the right approach of the web analyst towards other stakeholders.

Avinash, nice post… but what about display media? What about view-through, the impact of "view but not click" behaviors?

This post and model appears to be focused on the click, which for some brands is great, but for many others, is only a small portion of their experiences: they have social interaction with customers, they have sponsorships of digital experiences, they have display both bidded and guaranteed: where do all these view-based experiences fit into your approach and GA?

I guess you call this "MCA-ADC" in other posts, but any thoughts on how to fit it in here?

Michael: There might be a misunderstanding. You'll see Display Advertising in a bunch of my screenshots in the post, for example the very last one. You will also see that when I apply custom rules to my mode, I choose click but the screenshot shows that you can use impressions if you want.

I'm biased when it comes to View-Thrus, I do not believe in the party line coming out of most advertising platforms or from most Gurus. By default I give zero credit to View-Thrus because I'm being asked to believe on faith that View-Thrus by their mere existence are awesome.

My approach with valuing impressions (with no clicks) is to use controlled experiments to validate that I'm able to attribute a specific lift to those non-clicked on impressions (View-Thrus). Sometimes that comes out looking great, other times the impressions were a complete waste of time.

Digital attribution is great, and there's a lot to be learned from this excellent post, but there's a big missing piece. Discussions of attribution must be omni-channel to truly reflect the experience of customers, and accurately reflect marketing ROI.

Patrick: Just as digital analysts can't say that it is all digital, it is equally sub-optimal to say that digital is "way over estimated." Unless you measure it, you just don't know.

I share your stress on true multi-channel attribution, hence the post opens with an explanation of MCA O2S, AMS and ADC. There is also a link included, pasted below as well, where folks can learn more about the value of, and the challenge of, optimizing for all channels. (And while we are on this point, you can't do it with attribution modeling.)

Hey Avinash,
A great and very informative post indeed. It reminds me of my Maths Class on Volatility Modeling – EWMA and GARCH. :-) A lot of gobbledygook on hard core maths and modeling. ;-)

We have been using attribution to the final stage of conversion and I do agree with you that it is not the best method. It is almost like giving sales incentive to somebody who closes the sales, without any incentive to the earlier marketing efforts! But quite often I find this to be true!

Had a few questions:
– Based on the importance of each media (lets say if I am using the exponential decay), how would I decide my budgets? Do you suggest that if Social Media gets an importance of 2 and PPC gets an importance of 1, then I appropriately adjust my budgets?

– What is the best way to estimate decay factor? Just out of curiosity, are you guys also using MLE or other hard core maths to estimate? Or is google internally using it? I remember that in EWMA, again there were many models and JP Morgan had one that sold very well!

– We are combining a lot of factors and metrics here. Would this not reduce the overall visibility of decision making (I remember your post on moving from cumulative metrics to focus on individual metrics!)

Overall an eye opener that raises a lot of questions! :) Keep up the good work.

When you use the attribution modeling tool, as we did in the latter part of this post, the last column shows how much credit would swing (positively or negatively) based on your new model. Use those swings to come up with new media mix and experiment. You can just go out and try the new mix, but testing is better.

I don't know that there is one perfect answer for this. If you click on Edit next to Time Decay it will show you how GA is doing it, you can experiment from there.

There might be some misunderstanding. You are not mixing any metrics here, not even one. You are giving credit to all media touch points that lead to a conversion. You metric is still Conversion, it is still CPA.

All of these attribution models have the same fundamental flaw: they don't account for confounding factors. It's impossible to go back in time after the conversion event has occurred so as to see what would have happened had the user not been exposed to any adverting at all. Indeed, many of those users would have converted anyway. Thus, all of these models simply move the deck chairs around on the Titanic and don't get any closer to the real question: how do I quantify the level to which my media strategies are actually influencing users? The answer of course is to conduct control and test experiments and ignore most of the noise around attribution.

I think the point of the article is that it's a way of systematically generating hypotheses regarding peoples' responses to your marketing channels, which can then be tested.

I think your criticism applies to all web analytics data – none of it shows causality until you do controlled experiments. It's simply a way of developing a mental model of how website visitors act, using that model to generate hypotheses, which can then be tested by taking action and measuring the impact. Then you refine your mental model and start again. In this case, substitute attribution model for mental model.

I appreciate your feedback, thank you. I have to admit that I do believe that attribution modeling is incrementally better than what we have today (last-click). In as much I do disagree with your observation of the more obvious futility of deck chairs and titanic.

Even if you use a completely silly model like Linear or First Click, you are at least aware of how suboptimal Last Click is.

Maybe this is not right, but I've come to realize the value of solving for the non-utopian world I think people should live in. And not just on this topic, but on others as well you'll see me coming down from my high horse to solve a intermediate "just suck less" step so that I can bring people (some day!) to my utopia. It is so hard. :)

Given the suggestion by some data (seewhy.com/blog/2013/04/03/understanding-online-buyer-behavior-part1/) that 99% of users do not make a purchase on their first visit. Is it fair to weight a higher percentage of attribution to the first click?

In other words, if users have a natural propensity to not convert on a first visit, is it fair to say that the first click has more value than maybe we give it credit for?

Beyond that…. remember what you are saying with overvaluing first-click…. your first girl friend deserves a lot, or all, of the credit for you marrying your wife. I know that is a folksy metaphor, but think about it. Even if your first girl friend was amazing, even if she is the one who introduced you to your current wife and even if she massively pushed you two to get married, would you give your first girlfriend all, or most, of the credit?

Avinash can attribution be used to create lift in new customer acquisitions, aka the visitors who didn't convert and are not included in GA's attribution data. How can we use attribution to identify gaps in our media or is this primarily an ROI tool?

When you refer to click path are you referring to internal pages before conversion of channel click paths?

Ken: I'm not sure I understand your question. Only people who deliver a macro or micro conversion are included in the attribution analysis tool. The goal is to learn from them, get more people to convert via, now, better more informed acquisition strategies.

The picture you see included in the post is for the media touch point that drove each visit to the site.

Another great post from The Master! Really enjoyed the breakdowns and storytelling. Thank you again for feeding the hunger for more insight!

I agree that experimentation is the only way to test what the optimal budget allocation should be for a business and you've clearly demonstrated that attribution modeling can help design those experiments.

Hi Avinash, like the post, totally agree with the 3 step process (analysis must lead to tests otherwise it is a waste of time) and evaluating CPA (or ROAS).

Regardless of whether last (last) or last non direct is better as the default it definitely did cause confusion when MCF first arrived as the audience got confused when the 'last touch' number was different (at least in attribution modelling you can also do last non direct if you want to that is!)…

I am interested if anyone has leveraged the GA Premium and DFA integration to include DFA display campaign impressions within the attribution dataset, if so how much of an effort was this to configure?

I also would like to know how this configuration worked out and what insights were gained from it. My initial thoughts would be to eliminate the challenges faced in MCA-O2S, MCA-AMS (offline and cross-device) with Universal Analytics by marrying all touch points to a single user ID.

A potential issue would be the discrepancy between GA and DFA conversions due to the different pixel but if the conversions could be streamlined somehow I can see this being a great asset.

I LOVE the GA Model Comparison Tool. It seriously reduced the complexity of the enormous spreadsheet I had been using for the task ever since the release of Multi-Channel Funnels. The biggest headache of my modeling has always been Direct Visits / Brand Search, which correctly or incorrectly, I treat similarly. My question is, how much do you trust that your Direct visits are really Direct visits (typed in or bookmarks)? In your "Make Love To Your Direct Traffic" post, you refer to "corner cases" where odd browser configurations, etc. make for not-really-Direct Direct traffic.

Even after crediting Direct traffic with a 0-multiplier in the attribution model (highly debatable), I'm usually still left with 15%-20% of my conversions attributed to Direct (because it is the only source in the chain). Assuming I'm as diligent about tagging every last link to our website that I can get my hands on as you are (I make a valiant effort), would you interpret this as really that many people converting directly? As much as that thought makes a marketer happy, I've always been skeptical. How much of that is mis-attributed traffic? For example, I read that clicks to our http site from https sites show up as Direct. Facebook is often https … that alone could be large. Assuming that example is correct, how much other stuff like this is going on creating Direct traffic that really belongs to other sources? Are these really the corner cases or is Direct traffic mostly unidentified traffic with some real Direct traffic mixed in? The whole recent debacle with iOS 6 Google Searches showing up as Direct just adds to my concern, though at least the impact of that can still be guestimated and it only occurred in the last year – but the impact was huge. Thoughts?

Joe: With or without direct traffic issues, it is imperative that you use campaign tracking properly on all your campaigns to ensure that your MCF reports present a realistic view of your marketing touch points.

One good benefit of complete campaign tracking, as my post on direct visits outlines, your direct visits will also get cleaned up and only be direct visits.

On your second point… there are always things beyond your control and you have to make do with the hand you are dealt. If you want to figure out how much to attribute to direct, you can easily create a clean segment like "all direct visits with not via mobile platforms" and you can eliminate one of your hypotheses related to the cause of direct visits.

I've created a custom model using Google Analytics to separate all of my various traffic sources.

There are lots of steps since for some sources I want to break it into chunks like exact match vs. broad match, some I'm content to view it by campaign, and some I'm content to roll the whole source up into one group. I also try to grab as many variations of our brand name as I can and call that Brand Search.

After that, it is just a matter of playing with different attribution models as Avinash suggests.

I have struggled a bit with attrition cycles. Will spend some time with the Model Comparison Tool to better identify trends. That I think will better help me understand what is working. Looks very cool.

Looking at last click credit when each page through the process should get credit for conversion is a bit troubling. The Position based Model makes much more sense.

Going to take a bit of refining to achieve better detailed information.

This is mindblowing! Way back I did keyword attribution modelling to optimize bidding on keywords based on impression and first click. I found this to be very usefull in casting a wider net for people discovering my market.

Is there a way to dovetail multi-channel attribution to keyword attribution or should they just work as "layers".

Not sure how does it differ from simple concept of incremental response rate? If my RR from path A>B is 3% and RR from A>B>C is 5%, then incremental credit from 2% will go to C provided it's not one off case.

Does data driven attribution work only on this concept or any other complexity is also involved?

Jalpan: I encourage you to read the post again, especially the first part. It might help internalize the uniqueness of the problem we are trying to deal with when it comes to attribution.

At the end of the post I do make a very strong case for methodologies like media mix modeling where we try to compute incrementality. But it is important to understand that we should not solve for path analysis. How many paths can possibly exist?

For a client I just opened the data for, 98k conversions come from 33k paths. Even if mathematically you can look at all the paths and for each unique combination you can compute incrementality, how do you go about controlling the path a person can take? How do you execute your marketing plans?

There are a number of other challenges, but that should give you some idea of why this might be unwise.

Attribution modeling, as I mention at the start, is imperfect. But it is better than last click. (And controlled experimentation si better than, and more actionable, than attribution.)

If there is a website that is just trying to create brand awareness with a specific call to action as the only conversion tool. Would last call attribution be the best approach or is the time decay approach where you can give weightage to all previous page views before conversion would be a good approach.

In financial services and specifically banking, the customer journey transends from digital to call center or even a branch for complex purchases ( Mortgage, Auto loan etc).. Have you come across any financial insitution which tracks the metrics of referral sale ( customer leverages digital as a channel for research and start the app and saves it but completes it in the assisted channels) and influence sale ( researches online but starts and completes the application in the assisted channels).. Any pointers in this regard would be helpful…

Also given the proliferation of devices, what are the various means that you can track and assign a unique common identifier across digital devices ( tablet,smart phone, desktop etc) to dentify the prospect… This is also a problem in the FS space… My guess is that retail would have solved the same.. Any pointers will be helpful….

What are your thoughts on looking at consumer and business segments differently for Marketing attribution modeling, Would it really help to look at attribution modelling for b2b.

Here are my thoughts, Business segment of consumers are more influenced by personal relationship, sales/after sales experience, (and other related parameters) more than their experience online? Also their time to conversion, online path taken, impact of different digital media on business segment are so very different from consumer segment.

Secondly, most of b2b customers come back to you for purchase at regular intervals (based on need) if their past experience was good and if their requirements are met. Hence, their used of paid search for instance wouldn't necessarily mean a success story of your investment in that media (for b2b) and moreover paid search could've been used as a navigational medium to land on your page.

How do you demystify this to be able to do the right analytics & invest optimally to target b2b.

Let's leave the repeat purchases aside, simply because if you have a decent CRM system you should be able to track that efficiently and also tie that behavior either to the first purchase or subsequent marketing touch points.

The challenge is the first one because at that point you don't know who the person/company is and what they are looking at, doing.

On this front, just thinking of digital attribution, there is little difference between B2B and B2C. Especially because remember that what your analytics tool, Google/Adobe/IBM, is tracking is cookie/person based (rather than company based). Consider the same suggestions in this post for more on what to do.

Nice article, great insights and all so simple explained. I love your style to explain complex topics and I´m a great fan.

But I have problems in understanding your rule for picking the lookback window: "close to the upper limit of the number of days to conversion, excluding the outliers, plus a bit more."

Because even in the Time Lag Report I have the opportunity to switch beetwen a 0-90 days view. So here my problem starts. When I move the time controller the shares will change. So how can I find the right setting for this report???

In clear words…I have a broad product range with ragrd to the selling price. Low priced products as well as high priced products. Nearly 60% of Sales took place at the same day when I use the 30 days lookback window. Of course the share will descend when I raise the lookback window. All other days 1- ~ get around 1 %.

So do you mean with upper limit my 60% so should I take about 7 days? The differences in the Assisted Conversions report are huge when I take 7 or 30 days.

What would be your recommendation or what about the others on this site? How you are dealing with this problem.

Thomas: You can only get good enough on this stuff, you can't get to prefect. Part of this is the data we have. Part of it is that some desirable segmentation options might not be there (assuming they have a good impact). So on and so forth.

Please be aware of this. It is also important to point out that the things I do at the bottom of the Mind Blowing Model make some of the above complexity or perfection less important in terms of impact.

All that said…. I start with the average (around 75% of the purchases, to leave the outliers out there) Time Lag and use that for my initial models. After that, depending on the importance of the business, I use various custom conversion segments (on top of that report) to hone in on a better number.

Magnus: Here is a simple way to check (all the models). Go the the attribution modeling tool. Click on Select Model (on top of the table). Next to the model you'll see a little clipboard thingy icon, press that. It allows you to customize the model, but it also shows you what the model is doing.

If you do this for Time Decay, here is what you'll see: Half-life of 7 days. And "An assist click occurring this many days prior to conversion will receive 1/2 the credit of a click on the day of conversion."

I hope this helps. And now of course you can customize this to whatever you what! :)

Thanks for the tip! I still struggle to see how the value distribution would look like… So let's say we have only 1 conversion á $500 and using a Time Decay model with a 5 day half-life.

1. How the value distributed if there are only 2 interactions during a 8 day period, the first occurring on the 8th day before and the 2nd interaction (obviously on the day of conversion)?
2. "1/2 the credit of a click on the day of the conversion" – and how do I know what the credit is for that click on the day of the conversion?

The only place my opinion differs from you is with regard to the importance of first touch. If you are promoting a new product/service, isn't getting the word out there and getting traffic to your website of paramount importance?

I am not advocating for using first-in attribution, but rather for the value of position-based instead of time-decay.

It "feels" right for the touchpoints that drive initial awareness and close the conversion to get the majority of the credit. (And you can control the look-back window to make sure credit is not given to a first touchpoint that is really miles away from the conversion)

With regards to the amount of value… you are right, we can control it when we create our custom models. And it is not overrated to worry about it to some extent. For example in our Market Motive Mindblowing Model, above, and http://goo.gl/4PjiQh, you would play with the percent value in the top part, but it is the custom rules where the important bits are.

If I put in 10% and you put in 20% I don't think it matters all that much because of what we are deciding to do in custom credit rules.

1) Get the custom attribution into other parts of GA? OR
2) Export conversion data with the "new model" as actual transactions including purchase ID, so I can sync up with my database data (look at LTV by channel, etc)?

Thank you for the great feedback. I did spend some time on the custom segmentation, which is great. I basically grouped all non-paid interactions together to analyze the paid ones – very powerful.

In our business, we do care quite a bit about the first interaction because a) it takes a lot of time from the first time a customer hears about us until they try us, and b) it turns out that almost 100% of the following interactions outside of retargeting are unpaid.

However, being cautious about first click attribution based on your comments in the article, I was wondering if you know whether or not Google counts impressions on Google-run channels as a first interaction?

If the interactions were clicks/visits only, I'd feel much better about attributing towards them. Google seems to be (perhaps deliberately) vague on the topic, as I suspect they would get a lot more credit than other channels if it was impression based, as most other channels won't have impression data in GA.

Bjorn: I appreciate the first interaction, but only as you might have noticed in the article (and the comments, see my reply to Josh) I do not assign an irrational value to it. In my custom model I do appreciate certain behaviors (clicks more than impressions) and if the first click delivers that, I overvalue it in my model.

The distribution you see based on the click behavior. But as in my custom attribution model, you can value interactions for channels where Google has data.

Thanks for this post, very informative. I'm wondering that once you select any attribution model you're immediately "biasing" the data, no? Even when comparing one model versus another, I'm not sure I understand that CPA is actually higher or lower. Isn't it only that particular number because of that particular model?

Intuitively, it seems like the best way to get the most appropriate attribution would be not based on a model but based on the data. I know its not possible to connect all the dots since this is such a complex situation with so many variables but there's got to be some way to give credit to the right channels based on what the data says not based on the model were using.

In part two of your comment I feel like you are saying do part one (but there you express that that is not the right thing).

Let's step away, think of it this way…

Last-click attribution is silly (as is first-click, for the same reasons and more).

So you should do something better.

If you don't want to work very hard, go with time-decay type models. They are the next best step away from last-click. Your decisions will be better. (Not perfect, better.)

If you want to work hard, experiment with various custom attribution models. If you balance for factors that are in the top part of my custom attribution model in this post AND the bottom part, you will get much better answers.

If you are willing to work very hard, because it is worth it, you are that big of an advertiser/player on the web, you should in a persistent media-mix modeling practice inside your company. You eliminate the whole modeling thing, you will get very close to the perfect answers. You have to be willing to do this persistently, invest in people, process and technology to pull it off.

I'm so happy to receive your reply this afternoon. I've asked for a website from my frd and sign up a Google Analytics account to have a try this week! But I still have a question to ask you:

I still not understand how the Google computes the path. As you said, the path of the user online is so complicated, and you're not sure their browsing order. So how can Google compute the path? Is it based on the linear correlation between the channels or the conversions rates, or any other factors?
This question has confused me for about a week, and what answer I search on the Internet is just THE MACHINE/GOOGLE CAN AUTOMATICALLY COMPUTE FOR YOU. But I wanna know the calculation method and how!

Liberty: You can get help from an authorized consultant, they are quite affordable and can help you get on the right tack pretty fast. Here's a list: http://www.bit.ly/gaac

Here's a simple version of what all web analytics tools, including Google Analytics do… Each time you visit the site they capture the referrer and match it to your cookie id. Then when they create the reports, they are able to know the "path" you took during your visits.

Thanks for your awesome post. Despite not being a data analyst, I was able to use your post to make a customized attribution model (based on time decay) that does wonders and makes my strategic dashboard much more solid :)

I have one big question though.

Basically, I I'd love to use the same kind of model to attribute a value to each and every of my micro-objectives. Right now I kind of try to guess their value and see if that makes any sense. That works more or less… But I'd love to have something much more robust (there are things I like better than saying to my CEO "well, # of whitepaper download is worth 50€ because I think it's worth 50€" :p)

The problems is I can't find how to do that on GA (did I already say I'm no Data Analyst?). I do track most of my objectives there, but I can't find how to track assists/attribution for them.

In other words, I can measure "channels" attribution / assists for "micro-objectives", but not "micro-objectives" attribution / assists for "macro-objectives".

The post will help you figure out the best way to get the value of your whitepaper.

If you want to apply your custom (or the included standard) attribution model to only one, or a cluster, of micro-outcomes (or just the macro one), you can use the Conversion Segments feature in GA. If you go to the Model Comparison Tool in the attribution folder, at the top you will see something called Conversion Segments, just click on it and now you can create segments. Perhaps this will help.

Thanks for a great post! I have a background in predictive modeling and just starting to learn the ropes of marketing analytics.

I just have a quick question – do you have any thoughts/documented case studies about blending path length with the time decay model?
Reason I ask – instead of setting an absolute half-life #, shouldn't we be trying to gauge the true impact of each touch point?

And thanks again for the clear and detailed (and very readable, of course!) presentation of all the aspects.

Anirban: At the moment in Google Analytics you can only set the half life number for the time decay model. In my personal use of some of the advanced attribution modeling techniques, we have taken data out of Google Analytics and applied various strategies in a customized data environment. I'm afraid none of that work is in the public domain. My apologies.

If you wanted to gauge the true impact of each touch-point, please see some of the strategies I've applied in the bottom part of my Market Motive Mindblowing Attribution Model. I am creating multipliers for the touch-points that deliver better engagement, higher value (even if no conversion). You can consider using something like that in GA at the moment. That entire section is totally customizable and quite feature-rich.

I'm interested in using different attribution models to compare transactions and revenue. Therefore I have followed your steps & selected only Ecommerce transactions as the conversions to analyse in the Model Comparison tool, and also selected "Conversions & Value".

My problem is that I'd love to see this information at a more granular level (ideally by hour but even by day would be great). So, for example if I want to look at conversions & value using the Time Decay model for September, is there any way to see this information for Sept 1, Sept 2, Sept 3 etc without having to select each date individually?

John: A simple solution to your need is the combination of hiring an expert consultant and build out a technology environment where you can get this data, from your vendor's api, and build the reports you want. Depending on what you want to do this can be pretty cheap or pretty expensive.

Please allow me to focus on the higher order bit….

Remember the question you are trying to answer: Are people making so many visits every single hour that assists and conversions are happening every single hour and so I need to see that data every single hour.

Even for the largest ecommerce companies on the planet, this is not happening. So the quest for data delivers not actual business value.

You want right time data, not real time data. Look at your Time Lag and Path Length report, that has the answer you see as to what your right time is.

Avinash.
PS: If you need help of a consultant in your neck of the woods, you'll find a nice list here: http://www.bit.ly/gaac

1) If a significant percent of your conversions have only one path length (i.e. 80-90% of my conversions come from organic search), do I have an attribution problem? Is it reasonable to continue using "last touch?" (I've run models for first touch, last touch, and 40/20/40, and even split, and don't see much variance).

2) When we talk attribution, should I only be thinking about external channels (versus internal channels like "house ads" or "webinars"? Typically, are internal channels NOT included in an attribution model?

1. It definitely happens that for some rare industries and sites the path length is one. It would raise a small red flag for me and I would try to understand what is so unique about your company/industry/strategy. I would also try to use come Competitive Intelligence tools to see if I can understand what is happening with industry peers.

But, if you really just have one, you are likely going to be just fine with last-click attribution. Apply some segments to see if the 10% remaining is unique to one source (all people from china are in the 10% and they take 98 steps to convert!). If so you can use MCF to dive deeper, else you are fine.

2. Yes, attribution only accommodates for external sources. You can use advanced segmentation to analyze performance of your internal channels like house ads and webinars.

Love the post. We still refer to this on a daily basis when talking to clients and we're always excited when the conversion around attribution evolves.

Just thought I'd drop you a line and let you know about our latest whitepaper, "Media Attribution: Optimising digital marketing spend in Financial Services".

Our study involved 700 million media touch points, examining each advertising channel as part of the purchase path. It's the largest study of its kind to date.

What we found is that Facebook and display advertising should be credited for 830% more revenue than previously thought. Of course that's not to say search is irrelevant, the real insight is that multi touch attribution modelling is most definitely a step closer to accurate measurement, which of course means marketers can start to accurately understand media performance and finally, allocate media budgets appropriately.

You can download the study at the link above.

Happy to chat through any questions you may have. If we don't catch up before the holidays, have a safe one and speak to you in the new year!

A very nice article on Attribution. Would be great if someone can help me answer the below query:

Our CRM has the capability that auto-associates with the Opportunity for all such Contacts (within the Account) where an activity (related to the same product-We have multiple product Lines) was logged during the last 6 months. This will enable us to achieve the goal of associating all (100%) Opportunities with relevant Campaign (s) and then for all such contacts all the campaign associated with them will get mapped with the Opportunity.
We create an Opportunity at Contact level.

Now I am trying to figure out a Real time scenario where in a Contact attended a Product Seminar 12 months back(say Jan 2014) and then referred his/her colleague and then we started following up with the New Contact so sell the product for which the deal was closed after 7 months(July 2014). If we keep using the above suggested trigger then the ROI calculation wouldn’t be correct as the Original Campaign which is Product seminar campaign won’t get the credit for the Campaign as the Original member doesn’t get associated with the Opportunity and the Campaign doesn’t get the credit for the Deal.

Do let me know on how you guys can help me on such a situation? What are the best industry practices that you will suggest to associate Contacts with a Campaign for ROI attribution?

Kit: It would depend on the site, and the type of outcome the site is trying to deliver (for itself and its customers).

It is possible that you want to influence/increase the overlap between channels (say, after detailed analysis you notice that overlap tends to drive the outcomes you want). But usually, you simply react to how the consumers behave and then optimize your marketing.

It is very difficult to force people down a certain path. If you had to do it, it would best be accomplished by the ability to target unique users (or micro-clusters of them), the content you serve in response to their marketing touch point and the landing pages you create.

I use last non-direct click as basis (most close to regular GA reports) to compare with other attribution types. When I compare the results, for example position based or your Mindblowing model, then the direct traffic increases tremendously. Sometimes an increase of +100%. This totally clouds the results of the other channels.

How can I solve this issue, in order to make direct traffic less prominent?

Jeroen: I think you mean direct Conversions increase, not Traffic. The MCF reports don't show traffic.

With that clarity… It is not surprising that if you compare Last Non-Click Direct (where you throw Direct away as useless if any other campaign existed prior) to any other model that Direct increases. The amount of increase will depend on the type of website you have and how your consumers behave.

In my case it goes up from 28% to 45% (pretty big increase if you compare percentage points).

You cannot "make Direct less prominent" in this report. It is simply showing you what is happening on your site.

What you can do is ensure you have audited all the things required to ensure that no visit that should be stamped properly with a traffic source is being mis-attributed to Direct (say mobile app traffic). I have tips and more detail on this in this post: Excellent Analytics Tip #18: Make Love To Your Direct Traffic

I can't believe you're still replying to comments on this article, it's amazing and kudos to you for doing so.

Just wanted to debate your reply on Jeroens comment a little. If one optimizes on the Google standard reports, I would support his argument to use Last Non-Direct Click in the MCF reports… at first.

The action from attribution modeling is re-allocating budgets/changing CPA thresholds and the current target stem from the time when one (God forbid) optimized towards the standard GA reports. Because the model in use for attributing value in those is Last Non-Direct Click; one must also emanate the MCF analysis from Last Non-Direct Click…. right?

Magnus: Thanks so much for the kind words, I'm glad you find the blog to be of value.

Our friend Jeroen wants to fix the root cause, how to get it to be less prominent. This is only one way to do that. Fix the core, my post linked to above helps do that.

Now, let's think about direct in just attribution. Attribution modeling reports treat Direct as just another source (exactly as it should be treated). All non-attribution reports are wrong, IMHO. But, yes, you are right. If you want to do it wrongly, :), GA allows you to do that using Last Non-Direct Click!

I understand how these different attribution models work. But I need to understand how it deals with repeat conversion. From the customer journey perspective, if a customer made two (or more) purchases (conversion), and there may or may not be any channel exposure between this and the previous conversion, how does the second conversion get counted in the attribution model?

For the first convert, the Position based attribution would be 0.4 for TV, 0.067 for Social Network, 0.067 for Direct, 0.067 for TV, 0.4 for Social network. Adding all together I would attribute 0.467 for TV, 0.467 for Social Network, 0.067 for Direct.

But how do I deal with the second Convert? What if the second Convert is immediately right after the first Convert?

Or, I am guessing perhaps you would split this path into two paths:
So path 1 would become two paths:
1.1 TV -> Social Network -> Direct ->TV -> Social Network -> Convert
1.2 TV->Direct->Convert

If so, then how do you deal with the situation where you have two consecutive conversions, as the second conversion will not have any preceding channel?

I would appreciate it if you could explain my questions using Position Based and Time Decay attribution models. What’s the industry standard or accepted practice in dealing with repeat conversion?

KC: Think of it this way. The analytics tools noticed Simon purchased a toy car. The tool will go back and string up Simon's visits leading up to the toy car. It will find the referrers and display them in the relevant reports (say, Top Conversion Paths).

Now the next day, or ten minutes later, Simon comes back and buys a toy boat. The tool repeats above analysis. In this case if Simon comes back directly to the site and buys the boat, the attribution goes to Direct in the reports (say, Assisted Conversions).

Given this, your second set of strings represents reality.

Don't confuse above with attribution modeling (position, time decay etc). First, you identify the string, then you figure out which model to apply (and they will apply the logic exactly the same way in every scenario).

Recently I started analyzing "Attribution" and whether we have a problem and realized we don't have "Sessions to Transactions" any more but have "Path Length" which is based on "interactions".. so I'm confused, what is the difference between "Session to Transactions" vs. "Path length" report? (the goal is analyze if we have a problem in the first place, but I'm having a hard time understanding the difference between the two and which one to follow, note that I was able to create a custom report with "Session to transactions", here is the report fyi – https://www.google.com/analytics/web/template?uid=VNkbvy8pQnK8xV7eGiR1cw)

I'm glad we don't have Sessions to Transactions report any more, it measured imprecise data. So happy it is dead (is completely dead?).

The reports you want to use are Time Lag and Path Length in the Multi-Channel Funnels folder. They are not session-centric (flaw of the other reports), they are person-centric. They also include all touch-points including Direct (the other did not).

So if you want to know how long it takes people to convert, and what that distribution looks like, use Time Lag and Path Length.

The MCF reports in GA are "browser" centric, not person-centric. Even in User ID Views, MCFs are only a "kinda" person-centric view of the data, since GA doesn't properly visitor-stitch their data. (A failure in the product likely influenced by Google's "unique" relationship with a variety of governments and regulatory agencies). Almost any paid tool will provide you with a better "person-centric" view. Some, like KissMetrics, are reasonably priced.

That said, Avinash is correct to recommend the Multi-Channel Funnels as they provide a *better* view into your GA data set for the questions you're trying to answer. MCFs and Attribution Modelling in GA are powerful (and free!).

Thank you for this post; it is actually great, but I still think that Google Models are still unfair for all display media outside Google, because they do not measure those impressions that are still valuable and yet underestimated because they give the biggest amount of Brand visibility but cannot be measured with the tool.

We could risk on turning a very contributing conversion media such as lets say Facebook (to give an example) they may have not gotten any clicks, but that drives a big amount of users to convert via Direct or PPC Campaings. GA wouldn't even be able to identify that contribution and just give credit to any other media.

What do you think could be the best way to attribute those impressions to the model?

Elisa: Let us unpack a couple of different threads that are in your comment.

Forget tools and models for a moment. I do not believe without proof from a controlled experiment you should give anyone's impressions any credit. How do you know they are delivering value? Many display platforms show view-through (a way of doing what you suggest) and that is fine and dandy. But unless I run a controlled experiment where I measure conversions or brand uplifit or another valuable metric, I'm not giving anyone's impressions any credit. Facebook or Google or Yahoo!. I recommend all smart analysts follow this path.

The data you see in GA is because of the sources that are available to Google to integrate. As would be the case for Adobe or IBM or Facebook or others. You can also manually collect data from other platforms, take data out of Google Analytics using the free API, and do modeling using a different platform.

Depending on what you want to do, Universal Analytics allows you to send data back into GA. Please check out that option as well: http://goo.gl/VPGiP2

Excellent post Avinash, I needed to brush up on the essentials of assisted conversions and so on, and I got a gained a lot more on attribution, well written and broken down into digestible chunks, perfect for my short attention span!

Hi,
I notice that for many Time Decay Attribution model examples, the sum of attribution is always 100%. I want to understand why.

Say the half life is set to 1 day, and there is 1 conversion. And for this conversion, there is an exposure 1 day prior to the conversion, Therefore, this exposure gets attribution of 50%. But there is also an exposure 2 days prior, so that exposure should get attribution of 25%. This adds up to 75%.

The way I see it, decay of each exposure is independent of one another. There is no guarantee the attribution total will be 100%. The only way I can see it being 100% is if you always attribute the remaining attribution to the direct conversion. So, in my example, direct conversion gets 25%.

So if the time between conversion and exposure is really long, then attribution for that exposure should be small, and therefore direct conversion gets more fraction of the total attribution.

KC: The way the allocation works, each event is calculated based on time decay, GA will then normalize to make sure sum(events)=100%.

Put another way, using your example, the touch point 1 day prior gets 50% and the one 2 days prior gets 25%, then you'd normalize that and the 1 day prior touch point gets 2/3 of the credit and the and the 2 day prior one would get 1/3 of the credit.

Hi Avinash,
Thank you for your explanation. I thought about it that way too but I wasn't sure, because this means that in my example, the second (later) exposure will always get twice the credit than the first (earlier) exposure, this is because the half life is one day, so the credit of earlier exposure is always half of the later exposure (more decay by one day), so their relative share of credit is 1:2, and that it doesn't matter if the conversion happens on the third day, or 30th day. Is this correct?

Hy Avinash! Thank you for all the articles you write! They are great and very usefull for every online marketer!

I have a question ! Can you please tell me how the conversion from tabel of Linear Model Section is calculated? I mean the conversion from Multi-Channel Attribution Models's section article you wrote where you said

"And you are telling me that the Cost Per Acquisition for my display campaigns is not $201 but rather a lowly $155?Yes.

In that tabel – in the fourth column "Linear" are some conversions in rows – Paid Search – 467.16 and Display 171.80. How they are calculated?

Lucciana: As you change the attribution model, the conversions credited to a particular channel will change.

You can get a bit more detail about how this complex process works by clicking on the hat icon just under the date selector on the top right of the GA Attribution Modeling report. There is also a video there.

Thanks for the incredibly insightful article. I apologize in advance if you addressed this and I missed it, but at some point, does it make sense to remove the "unpaid" channels such as Organic Search and CRM email from the attribution model? If one of the main points of attribution modeling is to know where to allocate investment, than should we instead focus on channels with higher possible marginal returns on spend?

For example, if my company's attribution model found that most of the conversion credit should go to email and organic, I assume there is going to be a steeply diminishing marginal return on investment for those channels after a certain point. Taking CRM email–once you are sufficiently staffed and have the tools you need, you can't really spend an incremental $50-100k/month and expect a positive marginal return the way you could with a paid channel such as SEM. So, why not then focus the attribution modeling only on the paid channels to which you can funnel new spend and grow the business?

Niall: I'm afraid I (strongly) believe it does not make sense. Simply because, there is no such thing as an unpaid channel.

Just think for a moment the amount of resource you put into Organic Search. It is a huge investment in people (internal, external), time, opportunity cost and so much more. SEO costs money. So, why should we not hold our investment in SEO to the standards we would hold Display or PPC?

The second part of your question is completely different. Should you invest in highest value channels?

OMG, of course!

Compute the cost of every channel (including the "free" ones!). Compute the value delivered (short, long, profit, ltv, whatever you prefer). Then compute where to invest your dollars for best ROI.

If you want to do this really smart, use controlled experiments to figure out the incremental value of an additional dollar invested. It would be different for each channel. Use that to make investment decisions. So much fun.

In the last part of your kind comment. You don't have to assume "steeply diminishing marginal return", you can actually compute it and validate that assumption. Then make a decision. For any channel, of course, owned, earned and paid.

Jerad: I suppose you can use the strategy outlined in #8, Custom Attribution, to do that. Else you can take data out of GA and do it outside the platform (get help from a GACP if you need it, http://www.bit.ly/gaac).

1- Clicks Adwords add and visits site
2- Clicks Facebook add and visits site
3- Clicks a link in SERP, visit sites and achieves sign up goal
4- Clicks a link in newsletter and visits site
5- Clicks an Adwords add, visits site and makes a purchase

If we select ecommerce transaction from dropdown menu at the top of attribution model report, which channels get credit from purchase? Are channels in number 1,2 and 3 considered as contributors to purchase? Suppose we use linear modelling.

If we select all from dropdown menu at the top of attribution model report, which channels get credit? Are channels in number 1,2 and 3 counted twice one for sign up goal and one for purchase goal? Suppose we use linear modelling.

When you are doing when you click on the dropdown next to Conversion (on top right of the report) is simply telling GA which outcome to use to measure conversion. For me, it is Ecommerce or some of the Goals I've set up.

Each goal is only counted once per session, but if there was one ecommerce outcome and one goal outcome then the channels get credit for both (using the last-click and assisted rules above).

Emilia: Congratulations on doing the Market Motive course, so glad you found it to be of value!

As I'd mentioned in this post, there are no standard attribution models. There is no one size fits all.

In your case, as for everyone else, I would recommend reading #8 in this post and building out your own customized model.

One bit of additional clarity. It does not matter how many interactions are there. You simply create the best attribution model (based on the four steps in #8 in this post), and then let the chips fall where they may. You'll still make smarter decisions.

Hi Avinash,
We have a request for analysis that will provide answer such as "people exposed to the ad 4 times will visit your stores 3 times more on average than those who saw the ad once"

However, for any given person in our analysis, there are multiple exposures and multiple visits, and these events overlaps in a string of time, such as: visit1-visit2-exposure1-exposure2-visit3-visit4-exposure3-exposure4 and many more varieties. This person would have four exposures, and four visits, but it's not like all exposures occur first, then you have all visits.

So how should I analyze this kind of problem, as requested by our customer? What are some models and approaches?

KC: First, I wanted to share that the type of analysis your client is asking for is usually immensely not valuable. Even if you could do it, it is hard to imagine what you could learn from it that would be replicatable.

What you are asking for, depending on the tool you are using, will require custom coding to capture the interactions required, and it will require the user-id-override feature to be implemented via Universal Analytics (more here: http://zqi.me/uanalytics).

Thanks. That's what I thought. This type of question really is not going to be helpful to analyzing the effectiveness of an ad exposure. I will run by the customers and try to convince them of this point.

Avinash – overall I agree with your thoughts on Attribution, but I am struggling with 2 concepts that I would love to hear your (and everyone else's) opinion on.

1) You mention the importance of the final touch and reflect that in your model. I generally agree with this, but struggle in the case of affiliates. The affiliate programs I have worked on tend to be full of coupon sites. These sites tend to show a lot of last-touch attribution, but very little first-touch (due to the behavior of leaving an eCommerce site during checkout to hunt for a coupon and then returning). It is difficult for me to give those coupon sites too much credit for the sale as they are not really driving traffic. Thoughts?

2) Thinking about a micro-conversion such as lead capture. If a marketing channel drives a lead that ultimately converts from another channel, shouldn't the visit with micro-conversion event get some sort of boosted attribution? (You would be giving a large % of the credit to the email channel that the customer converted from as a last-touch, but the lead would not have existed without the first channel. . .) Thoughts on this, and how this could be done operationally?

1. It is very difficult to judge these things, especially if the point of view is that these gosh darned affiliates are costing us money as these people do Google searches and come back with the coupons!! Who can say if the person would have converted at all. Maybe no affiliate, no conversion. Right?

The bottom-line is that we can make these types of arguments for email, for seo, for facebook. So I don't feel anything terrible about using the same model – let the chips fall where they might.

If you really want to test this, the best way is to do a controlled experiments. You can do a crude one. Turn off all affiliates for 30 days. See what happens. Just make sure that in that 30 day period, as much as possible, keep the other factors the same.

2. Remember, you are trying to optimize for the portfolio, and I think of judging macro and micro conversions in that context and use economic value.

If you really want to do what you are worried about, good news is that it is possible. I wrote about User-ID Override feature of Universal Analytics. If you use that, you can actually track back the ultimate conversion to the Lead and be even smarter about your media budget optimization. The Magic of Universal Analytics: Strategy, Tactics, Implementation Tips

It seems that APAC marketers are with you on the love for the customized attribution model. A recent study from Econsultancy and Datalicious found that 41% of marketers across Asia Pacific say that custom modelling attribution as the most effective.

The study, State of Marketing Attribution in Asia Pacific, also revealed some interesting stats about marketing attribution in the region. The study found that while 71% of marketers believe cross-device consumer behaviour is increasingly important, a lack of knowledge stops marketing departments from actioning attribution implementation. Two-thirds of marketers do not carry out any form of attribution.

"This model is based on the concept of exponential decay and most heavily credits the touchpoints that occurred nearest to the time of conversion. The Time Decay model has a default half-life of 7 days, meaning that a touchpoint occurring 7 days prior to a conversion will receive 1/2 the credit of a touchpoint that occurs on the day of conversion. Similarly, a touchpoint occuring 14 days prior will receive 1/4 the credit of a day-of-conversion touchpoint. The exponential decay continues within your lookback window (default of 30 days)."

It is not directly comparable to a custom attribution model (as in your case).

thanks for your reply. Based of your article, I have created two custom attribution models and they seem to work ok. One last thing that concerns me is whether it is possible to import data from these models into Google Spreadsheet via Google Analytics Spreadsheet Add-On or via SuperMetrics.

Really good article. A lot to take in. Surprisingly, this is just about as relevant as when it was written. My question is this:

For small businesses that don't have a ton of channels or traffic, I would think that the last click is the first click is the only click, leaving most path limited to one. If we dismiss fortune 500 companies from this conversation and focus on the 97% of all businesses in America that have 20 or less employees, how bad is last click attribution really?

When you take into account the law of averages in that some channels will give up credit to others and take into their overall traffic as a percentage of the traffic to conversion mix, isn't it easier for these businesses that don't have 25 person marketing teams to do the analysis in aggregate?

Patrick: Here's the important bit. You don't have to guess/worry if it applies to you or not. Simply look at your own data, decide what the optimal path is for you.

Start with the Path Length report in the Multi-Channel Funnels folder first, how many do you see with 1 or 2? A majority of the numbers there mean this is less of an issue, small mean there is an issue. Then look at the Assisted Conversions report, get a strong sense for the optimal value added by each channel. Go from there.

To your last paragraph… You don't really need to worry about the size as everything even you 0.5 Analyst needs is pre-built. Just go to the Attribution folder, click on Model Comparison Tool, pick Time Decay, and you are in business. Very little work.

If you are a large company, you can do more. But, for a small company, it is all there ready to eat. :)

Brad: Unless I have an alternative source of information, I do not take into consideration the value of Impressions for my Performance Marketing efforts.

For Brand Marketing efforts, I mentioned this in the post, I'll leverage Controlled Experiments to figure out the brand (or even performance) value of Impressions. For example, people seeing my display ads have a xx points more favorable impression of the brand or are y points more likely to visit my site directly and buy. If there is, I'll have a multiplier for impressions. If not, then no.

Bottom-line, I'm unwilling to take it as a matter of faith that the impression of an add necessarily had any business value.

This is an amazing insight into the world of Attribution modelling. Very informative & ready to go guide for beginners.

Since the entire attribution modeling works for click based attribution (while in display based campaigns there are hardly any clicks but huge view through's) How does we take into account the efficacy of these campaigns!

Great post to find as someone who is getting even deeper into web analytics. I think this may take me a while to get my head around and will require some 'offline' analysis to be performed but it'll be worth it in the end. The way our analytics tool is used at the moment is like having a great car but only using it to listen to the radio but I'm aiming to change that!

One question, and I appreciate I'm three years late to the post but hope to get an answer from someone anyway – cost data. I'm assuming that when you calculate the costs of each channel, you're taking into account salaries, consultant fees etc? Otherwise I'm not sure how to calculate organic search. We don't have anyone assigned to content marketing – the way our company works at the moment is that blog posts get just pushed when ready, without any sort of agenda. It's another thing I want to change but would love to hear thoughts on this sort of thing.

Nicola : If you have your Google AdWords and DoubleClick Display platforms linked up with Google Analytics, you can definitely take cost into account. You'll see CPA in the report itself, please see the screenshot under the Multi-Channel Attribution Models in the post.

I am thinking of slight parallel of attribution modelling, I have a customer who wants to measure effectiveness of TV ad campaigns for a particular TV program. Can i use a attribution model for Viewership to understand the effectiveness of the campaign??

Hey Avninash, I have a quick question about the Last Non-Direct Click Attribution Model that is in the standard GA reports.

Obviously if you look at the Direct Channel in the standard reports it has transactions/revenue associated with it. When exactly does it credit for transactions with the Last Non-Direct Click Attribution Model? Is it only if there was no prior campaign interaction? Is it a catch all for Google Analytics if it can't identify the source? WHAT IS DIRECT TRAFFIC in GA!?

If in Google Analytics I go to Acquisition > All Traffic > Channels, the e-commerce numbers I am seeing are based on the Last Non-Direct Click model.

However, the Direct channel (which is being reported via the Last Non-Direct Click model in this report) has received credit for over 50% of the companies transactions. So my question is, if with the Last Non-Direct Click model, the direct channel is still getting credit for transactions – what are those transactions? Because technically, with the Last Non-Direct Click attribution model – shouldn't it be getting credit for 0 transactions? Unless maybe there were no previous touch points?

First of All: Love this post – and your way of writing in general! So good when great learnings come with a good sense of humor :) Thanky you for that!

Now my question:
I have a specific problem with exporting the Custom Model Data and hope you can push me in the right direction for solving it:

We created a custom model which gives value to the last paid click -channel (for sure, we do not look only on this model for evaluating channel attribution, no worries :)) and I would need the Model data exported per day for each channel for a quite big time range in order to be able to use it in our nice custom reporting – but when I select a data range it will only be exported accumulated per channel for that data range…
I already use the MCF API where the data per day of the different models can be exported easily into a spreadsheet, but that only works for the not self-defined/not custom models and does not include the data of the custom one …

well, i guess I could maybe write a script to request the data from the Api but….I honestly have not enough knowledge doing that…

Any advice what allows me to export the data of the cutsom model per day?

This has been a really great article for me to start learning about the many different ways to evaluate a company's marketing efforts. Thanks for putting this post together!

I work in an industry where roughly 84% of our customers have a path length of 3 or less with 47% of those conversions occurring with just 1 path. Also the assisted / last click or direct conversion overall ratio is just 0.53. With what I read in your post it doesn't sound like we would have much of an attribution problem, but I'd like to know what your thoughts are on figuring out which model might best suite a company in this situation.

There is so much information out there and I've been using GA for about 2 years now and am trying to take my skills to the next level.

one thing i would want to consider using this for is adwords keyword bidding to get a true value of a keyword. is this something to consider? i'm in the middle of some analysis and using my model 50/20/30 split some keywords with really good 1st or last click revenue would have a reduction using this model and i'm wary this could be a mistake.

am i correct in using an attribution model for keyword bidding or is this risky?

Chris: Path Length analysis for AdWords is quite important, and in turn attribution modeling is a valuable exercise to consider.

You should be able to use segmentation to isolate Paid Search behavior. Also, start with the Assisted Conversions report to get a sense for how much attribution will influence your decisions. The Assisted Conversion column for the Paid Search row is what you are looking for.

While I enjoy the Time Decay based attribution model for my own analytics, I think the Position Based Attribution model could work awesome to either agree on the importance of each part of the funnel with a client or add in my own bias!

I do not see much use in many of the attribution models (first-click… really?) and I will continue with keeping consistent with the use of 1-2 models for all clients.

Tyler: In the standard models, Time Decay is likely your best shot. You'll notice in the custom attribution model above, the foundation is built of Position Based.

Our goal should be to end up with Data-Driven Attribution Models. There is simply no way that we can understand the complexity across hundreds of thousands of visitors and their, then, millions of touch-points. Machine-Learning helps us get to the right answers optimally. No human biases required. :)

You have mentioned quite a lot about Attribution Models in the Multi-Channel ADC space. Can you share your thoughts around controlled experiments. I am asking because as I read through the article, I kept wondering why are we doing this analysis (first, last, even or decay).

For context, I work in the Financial Services world, so lets assume we are offering a Personal Loan online. Customers can come through Direct, Organic Search, Paid Search, Display and Direct Mail.

What do you think of the following approach:

1. Setup a design of experiments, where for different subsets of population or time instances (so that I don't impact my business), I will fully or partially block a different channels individually, and measure the impact on the Click to Buy funnel for the subset. This should tell me the incremental contribution of particular channel.
2. Based on the overall impact analysis, I assign budgets to these channels.
3. I repeat steps 1 and 2, and thrown in some "randomness" element in step 2, in case there is a local minima problem.

Can you share your thoughts on this approach, and point our flaws or refinements.

The broad strokes of your outline should help you get going with experimentation. You can glean additional strategies from Design of Experiments thinking, which will make your experimentation more robust.

This is great stuff, but don't undersell the importance of including offline efforts and things like competitive or environmental issues that affect your results. A Unified Measurement approach is game-changing.

I was into building Email and DM predictive models for a long time and now I have moved to Digital Analytics and my burning question is to how to build product propensity models (somebody likely to purchase or not) on visitor information.

Do you have any idea or point to a resource that helps me to implement advanced statistical models on Digital analytics.

Sravan: In your case the data from the attribution models will be a big source for your predictive models.

For example if you look at what the Data Driven Attribution Model is demonstrating, you are getting the role that Email plays in driving a macro or micro-outcome. It shows the position, it can show the outcome, you can segment the data if you want. Depending on how savvy your analytics implementation is, you can get this at an anonymous level (less useful) or an individual human level (a lot more useful).

All this will be critical data for your propensity models, which will rely on other data as well to get to the goal you've set.

Thanks Avinash!! I think i should have phrased my question in a different way.

So, what I'm trying to do is to replicate product propensity models that we used to do for EM marketing, it is doable over there, because you know the customer, their purchase behavior, the trend itself. That enables us to predict the future behavior.

Whereas in, Digital analytics, it is all about visitor which is really volatile in nature and there is no easy way to predict out of the visitor behavior, since it is not the real human but someone anonymous. So, how can we build predictive models on visitor data?

Sravan: I encourage you to work with an established consultant with expertise in digital analytics.

They'll be able to share how you can collect past behaviour of everyone who becomes your customer, rich data-set where outcomes are complemented with their behaviour prior to purchase. You can then use patterns you identify to predict propensity to deliver an outcome from a visitor you have never seen, a visitor you have multiple times and past customers you are seeing again.

Sharon: Things seem to have evolved so much since I wrote this post. We have an even stronger sense of how consumers are actually behaving.

My recommendation now, regardless of the tool you use, is to skip all the standard and custom models and jump to Data-Driven Attribution. It is also free in tools like GA. Just skip the whole let's guess what might be going on before we use Machine Learning to realize what's actually happening. :)

I totally agree with all your recommendations except one I would like to discuss with you.

With the "position based" attribution model you adjust it 10-50-40, however I always recommend to attribute more weight on the first touch, because if you focus on the low funnel, you are not going to value the strategies that prospect and achieve new traffic.

I would understand if you tell me it is because you prefer to be super profitable being conservative, but f.e. for a launch of a new brand or product, it is better at the beginning to reward the tactics that increase audience impact & reaching new users that later we will retarget and try to convert. Does it sound good to you??

But here's the important part, your custom attribution model, weather you do it my way or your way, is just a good way to get going. Pretty quickly you are going to migrate to using the Data-Driven Attribution model which analyzes millions of touch points across tens of millions of your users and uses Machine Learning to create the best model for your site. This one won't have our biases (like if early clicks should have more weight) as it has actual user behaviour data. Hence, the best way to go.

My trouble is to proof to my clients the value of Display Programmatic (and even FB) where typically we see conversions coming post view…. GA is great to track post clicks conversions but it doesn't "see" post view conversions. If I base my attribution modelling only on post clicks conversions then the upper funnel channels will look always bad. Surely a Display impression that helped driving a conversion has a value?

Just wondering what are your thoughts on this please? how can you give value to those post view conversions?

Excellent post as always, thank you very much. We are starting to explore the realm of attribution more and our agency, who manages a large chunk of our digital activity, is keen to run with a linear attribution model, which I questioned as I thought this model may just be a way for them to justify the under performing channels like display, which via remarketing could be more likely to appear in the linear attribution model and given weight.

Renee: If you are using custom attribution modeling, there is no way to avoid opinions. And, with our perception of reality always biased, it is hard to argue one way or the other.

Think of the decision you are trying to make as one step in a journey. Then, you can easily figure out where linear falls on the way and if you don't trust it (and you should not over the long run!), you can figure out how to get to the next step.

Seriously this is a great description of how to think about attribution models and reporting but, and it is a big but, really what is the policy that this analysis is going to drive?

The attribution model as is, tells us nothing about how it will respond to change – which is exactly what we are looking for.
In order for this stuff to work, at each point in the process were we might try to affect behavior, we need to embed at least a null option, so that we are working with a decision process, rather than just a chain, no?

[…]
So while attribution vendors are getting very good at tracking exposure to banner ads and search – and doling out credit – they are actually following cookies, not people. In a world where more than 60% of us own a smartphone, almost half own a tablet, and 82% of global consumers in a recent Microsoft-led survey said they like “multi-screening,” the damage in terms of inefficient marketing is major.
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[…]
Google “Digital Marketing Evangelist” Avinash Kaushik lays out on his personal blog what he thinks makes effective multichannel attribution modeling on his site. He begins, “My macro goal is to make you dangerously informed. By the end of this post, if you pay attention, you'll know the often hidden nuances and you'll be dangerous to any analyst/consultant/vendor who walks into your cubicle/office …” Read dangerously, my friends.
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Danielle Uskovic‘s insight:
I’m a big fan of Avinash Kaushik and avidly read his blog. Attribution modeling is a hot topic for brands and this article looks at all types of measuring this beast.
See on http://www.kaushik.net
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[…]
n the world of PPC, it’s always great when you see leads/sales coming in through your campaigns: search, display, remarketing, social, etc.. But when it comes time to make optimizations and allocate budget dollars, you need to know which channel influences your customers the most. That’s where Avinash comes in. His article Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models is not solely a PPC specific article, but all of this points can be really helpful when setting up attribution modeling. He even walks you through how to do it! If you’re tired of bid and ad copy changes and want to step outside of the box for optimization inspiration, then grab a coffee and hunker down for this long yet informative post.
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What I felt today was different, though. It came when I was looking for some info on a piece of software I use (Google Analytics). I started at the website of a well-known expert in the field, Avinash Kaushik at his The Good, The Bad, and The Ugly piece. That article led me to his Definitions, Models, and a Reality Check piece. That article led me to his Tracking the Online Impact of your Offline Campaigns piece, and that article led me to David Hughes’ website because he coined the term “non-line” that Avinash uses (it means marketing efforts that exist both on- and offline, like the color of a logo).
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It’s a common marketing question that has several possible solutions. There’s last click attribution, where the last step gets all the credit (the search ad). There’s first click attribution, where the first step would get all the credit (the TV placement). There’s also linear attribution where you give equal credit to each touch point in the process. Avinash Kaushik put together a wonderful post detailing some of the various types of attribution models.
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Solutions such as Google’s pack in as much data as possible about a particular users’ exposure to your ads — what, when and where — and look at user-level data across vast swarms of humans. It then constructs a best-fit description of the info it has, saying things like, “Display ads contributed 21.2% ($45K) to the campaign’s success.” But as Google’s resident evangelist Avinash Kaushik recently warned, “There are few things more complicated in analytics … than multi-channel attribution modeling.” So in the words of a musical team from the pre-Google era, don’t believe the hype. At least, not all of it.
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Therefore, basing a marketing budget solely upon this method would undervalue the contribution of social media to the conversion process. Google Digital Marketing Evangelist Avinash Kaushik wrote an excellent blog post on attribution modeling, addressing these issues. He opined the Time Decay Attribution Model does a fairly good job above and beyond the last click, and I would agree.
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[…]
If you want to read up on multi-channel attribution models, I recommend Avinash Kaushik's work Multi-Channel Attribution: Definitions, Models and a Reality Check and Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models.
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Secondly, Cross-device tracking & attribution modelling are here to stay. The holistic value of search across multiple devices and channels and; the complex paths to purchase are likely to become an important aspect of PPC budget allocations in 2014.
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Multi-channel, also as the name suggests, spreads out the conversion value to better represent the customer’s relationship. My personal favorite method, and as Avinash Kaushik points out as the easiest to go astray, is the Position Based Attribution Model. This particular model is flexible, breaking down the total conversion value into two lump sums (equal for first and last) and then evenly distributes the rest through the other conversion steps.
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Favorite: “Don’t Silo Mobile Marketing” – This one could not be emphasized more. One of the key elements I’ve seen in mobile marketing is its presence in the conversion path. In plain English this means people may find you with a mobile search but convert on a web browser. Some people research a purchase several times before they convert. You want your product or service to be where their eyes are. Even when mobile doesn’t convert as last click it is very often present somewhere in the funnel. If you want more information on how this process works you should research multi-channel conversion attribution models. This is a great article by my favorite author on that subject: “Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models” by Avinash Kaushik.
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Now, if you read the first paragraph, then continued onto the second paragraph, and you've respected the basic tenets of literacy by moving onto this, the third paragraph, you might already know where I'm going with this preamble. We have entered the age of attribution modeling, where we try to right the wrongs inherent in these tools by affording a certain slice of the attribution pie to all the channels that participated in the conversion (i.e. making the visitor fulfill a goal on the website). Check Avinash Kaushik's nice introduction to the topic here.
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That’s a really big question. The best answer I have is to direct folks to this excellent post by Avinash Kaushik. While it is a fairly long read, it is very well written and explains a very complex topic in very clear language.
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*** You still have to be using the right model. This is the point in the course where my brain exploded all over my laptop, so I will hand you over to the wonderful Avinash Kaushik for enlightenment.
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When you’re looking at tightly cut back budgets, affiliate marketing is often the first channel to get the chop. Before you consider that, though, let’s get our attribution on. I recommend getting a strong cup of coffee and setting aside a solid day to work through each example given by Avinash in this superb post on the basics behind attribution models. Try out a few examples and see how they would influence your decision, specifically, on cutting back a channel that’s currently driving revenue.
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You'll want to track all the usual online metrics, including conversions from awareness channels. While direct conversions might be relatively small, be sure to look at multichannel attribution. It's likely you'll see many conversions coming from organic or direct traffic that were influenced by your awareness efforts.
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Of course there are many marketing attribution models you can follow. But that’s a post for another day. For now, just continue business as usual, making sure to follow a consistent tagging structure. Spelling and capitalization count! Be sure to establish and follow patterns to avoid a big mess when it’s time to analyze your efforts. After all, “LinkedIn” vs. “linkedin” will show up as two separate records if you get lazy.
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Some marketers opt to give the first “touch point” rather than the last “touch point” 100% of the credit. So in the case above Facebook would get the “goal” for being the first source to let the buying customer know about your online course. However, this is problematic as well because it ignores the steps in between. Another blog put it perfectly when the author wrote “First click attribution is akin to giving my first girlfriend 100% of the credit for me marrying my wife.”
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If you’ve read up on attribution modelling in the past, you probably already know whatâs wrong with the default models. If you havenât, I recommend you read this post by Avinash, which outlines the basics of how they all work. In short, theyâre all based on arbitrary, oversimplified assumptions about how people use the internet.
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In this scenario, a few things would happen to the KPIs of most e-commerce companies: The main website’s conversion rate would look better during the day of the campaign, since some customers like Alex logged on to it directly to purchase. The conversion would be attributed to the “direct” (also known as organic) channel, since that’s the last channel the customer came in from.
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On the other hand, by using a multi-touch attribution model, the eventual sale gives credit to all the marketing channels that influence your customer during their awareness, education and consideration stages of the buying cycle. Plus, since Social Media is only one part of the marketing mix, knowing how other touch points along the sales cycle influence your prospects will provide insight into understanding how you can accelerate and optimise the entire process. There are many tools out there that measure multi-touch attribution, such as Marketo and even Google Analytics. Here’s a useful article to learn more about the topic.
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[…]
If you’ve read up on attribution modelling in the past, you probably already know what’s wrong with the default models. If you haven’t, I recommend you read this post by Avinash, which outlines the basics of how they all work. In short, they’re all based on arbitrary, oversimplified assumptions about how people use the internet.
[…]

[…]
There are quite a few attribution models but they are more complicated and I don't know anyone who uses them in the gaming industry. I think it would make sense to use them for the travel industry when you want to know whether a hotel is good or not before booking so you read reviews and information online from multiple sources before making your final decision. In which case the Customized/Personalized Attribution Model looks great but I personally only know this from following Avinash Kaushik (Google's Evangelist), I don't speak from experience
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Fact #3: Consider that 50% of all shoppers are in market 90days or longer, and visit >20 automotive sites prior to purchase. Your not looking for the lead source, your looking for the MOST POPULAR paths to your store. In other words, if you obsess about lead source, what of the 19 other sites the shopper was on prior to sending you a lead? Where did they go after they sent you a lead? Look into attribution modeling*.
*https://support.google.com/analytics…/1662518?hl=en
*Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models – Occam's Razor by Avinash Kaushik
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Some readers were surprised this post didn’t discuss multi-touch attribution within digital channels. For those interested, this post provides a good overview of good and bad multi-touch (digital) attribution. I recommend starting out with the time decay model, where the media touch point closest to conversion gets most of the credit, and the touch point prior to that will get less credit based on a smart and simple algorithm.
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The __reff cookie takes care of this part by allowing you to actually save all the referrers that brought the user to your website before he converted. You will actually be able to apply some advanced attribution modelling because of this, though that would require some advanced database skills on your side.
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So you can see the logistical challenge. It gets worse. Of course, the model wouldn't be accurate unless it tracked people across all their devices, also, so that it knew that Martin on this version of Explorer is the same Martin on that iPhone and on that tablet and booting up that app on his X-Box One. And I haven't even mentioned the nontrivial (nerd-speak for "really, really hard") problem of achieving statistical significance, and that experts don't agree on a formula or algorithm to use. (Google introduced one last year based on game theory. For a great overview of attribution models in general, see Avinash Kaushik's blog post.)
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It’s possible that some of the shoppers rummaging through the marked-down sweaters in your bargain basement saw your banner ad this morning. Possible, but not likely; it’s more likely they’re there because they know exactly when you mark down sweaters every season. Complaints about revenue attribution usually center on the “last click” versus “full-funnel” debate, a tiresome argument you can avoid by insisting on measurement through market testing.
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These and other data-driven attribution models are many and varied. And they can be fairly challenging to grasp. A great view of all the options is in this post by Avinash Kaushik – author of two best-selling books on Web Analytics.
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Time to Conversion (or sales cycle length) – Somewhat tricky and varies widely across industry. This aids sales teams to see what’s in their pipeline. Attribution Modeling is a great way to grasp the sales cycle. But be careful and don’t over-emphasize reducing time to conversion.
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Avinash Kaushik has written extensively on the subject and gives a good overview of multi-channel attribution modeling to get you started thinking about what is best for you. Whatever path you choose, make sure that you learn what efforts provide the most value. That way, when it’s time to review your strategy, you can do so in the most educated way possible.
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Taking this analysis of ROI a step further, using the multi-channel funnels assisted conversions report, we can begin to understand which channels contribute to conversions overall and how: Now we can explore the next level of analysis by attribution modeling, so we can grow resources for channels based on their overall contribution. For more on modeling, check out this post on multi-channel attribution by Web analytics guru Avinash Kaushik.
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For marketers, this information is critical to understanding where our customers are, where they are interacting, and, therefore, where our time needs to be spent. If we only look at last-click conversions, we do not truly understand the customer journey. Here are a few resources worth checking out: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models
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You can read more about the different types of Google Analytics models and how to use them here. Also, Avinash Kaushik, Digital Marketing Evangelist at Google wrote a blog post describing how to use the different reports. The biggest difference between using reports on Google Analytics vs. HubSpot is that you cannot connect the report back to specific contacts unless you use HubSpot. That means you cannot apply attribution trends to specific personas, contact groups, lifecycle stages, or other categories that are relevant to your business. So when deciding on what report to use, take that into consideration.
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[…]
You can read more about the different types of Google Analytics models and how to use them here. Also, Avinash Kaushik, Digital Marketing Evangelist at Google wrote a blog post describing how to use the different reports. The biggest difference between using reports on Google Analytics vs. HubSpot is that you cannot connect the report back to specific contacts unless you use HubSpot. That means you cannot apply attribution trends to specific personas, contact groups, lifecycle stages, or other categories that are relevant to your business. So when deciding on what report to use, take that into consideration.
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[…]
The overwhelming number of attribution models available further complicates an already thorny issue. As a hypothetical example, consider your marketing team has decided it’s time to switch from last-click attribution to another measurement methodology that better accounts for cross-channel conversion paths. There are many multi-touch attribution models that would represent an improvement over last-click, so which is best?
[…]

[…]
In this scenario, a few things would happen to the KPIs of most e-commerce companies: The main website’s conversion rate would look better during the day of the campaign, since some customers like Alex logged on to it directly to purchase. The conversion would be attributed to the “direct” (also known as organic) channel, since that’s the last channel the customer came in from.
[…]

[…]
The attribution of value in each interaction leading up to a sale is complicated. Thankfully Google Analytics gives us tools to test and explore different attributions models. There maybe more value in your online marketing Campaigns than you have been giving it credit for. I will leave it to Analytics Ninja Avinash Kaushik to explain this in more depth and how it can improve your bottom line. The Good, The Bad & The Ugly of Attribution Models.
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GA has a number of default models for comparison, as you can see on the left here. I won’t go into great detail about all of them, if you want more explanation I’ll send you to Avinash Kaushik’s really excellent article on the subject (an inspiration for a lot of what I’m covering in this post).
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For an excellent piece on the topic of Attribution and Attribution Modelling, visit the blog of Avinash Kaushik, however we are here to discuss Universal Analytics as whole. Tracking onpage and offpage metrics in analytics has long been hit and miss, the main methods being to create specific landing pages/urls (for TV adverts use http://www.tvad.com, which then redirects to http://www.thingswewanttosell.co.uk) and bespoke tagging parameters within urls (eshots or general email footers).
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As Digital Evangelist and Google Guru Avinash Kaushik points out on his blog, “Historically, all tools used last click attribution because the one thing they could confidently say is what drove the converting visit. And they did not have the technical horsepower to do Visitor-centric analysis. Both these problems are solved now.”
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In online advertising, for instance, it is not difficult to measure the conversion rate of a product purchase after clicking on a particular banner. But does it give you a whole information, whether you should optimize this channel only? People usually don’t buy after first sight. They get in touch with your product several times, from different devices, even offline, before they make a final decision. And that can be a complicated and lengthy process, not so easy to grasp.
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Heuristic models: These are rule-based models, and are a very good first step towards understanding the value of your marketing efforts. One place to get a description of different rule-based models and how they work is this blog post by Avinash Kaushik, "Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models."
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Attribution modeling. What it is and how it works. You don’t have to be a genius at it, but you do need to know your options. Go into Google Analytics and play with it there, just click around. I highly recommend my colleague Michael Wiegand’s PPC Attribution webinar deck with Hanapin Marketing as an intro with a fun Game of Thrones theme. Plus, read Avinash Kaushik’s diatribe on Good, Bad, and Ugly Models and you’re on the right path. Pun intended.
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Only recently, I have seen marketers get more and more interested in various other models – right from time decay and U-curve to custom models where a mix of channel and the time spent together decide the contribution of that channel. And it’s not possible to talk about attribution without mentioning Avinash Kaushik. He has been at the forefront and been educating the world about attribution. Get more of the details about attribution modeling on his blog.
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Unless the vast majority of our customers are converting in the same session as they click on our Google AdWords ad, last-click attribution will provide a misleading picture of your campaigns’ ROI. In addition to our blog series, Avinash Kaushik has a great introduction to attribution models that’s a good starting point. The bottom line is that these reports are just a few clicks away in Google Analytics.
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All these baseline models function on assumptions about the customers. There are options to create custom attribution models, and as marketing technologies become more sophisticated, companies should be able to see the attribution model vary with every customer that is closed. The attribution of value will follow more closely the content consumption of each prospect in order to give a company more actionable information about their marketing channels.
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There is no limit to the number of combinations that can occur, but you’ll likely find that some are more common than others. This data can help you decide where your marketing dollars should go, and how you should approach brand new prospects. Admittedly once you introduce this feature into your company, revenue attribution can become a bit of a headache. However, the value of this data is worth any temporary complexities.
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Bertaut recommends that advertisers build a bespoke attribution model suited to their own business objectives, which can then be “tweaked” on a campaign-by-campaign basis. Advising further on this, he highlights a recent blog post penned by Avinash Kaushik, a Google co-founder, that debates the merits of various models.
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Attribution and life-time value metrics can be tricky. This is more of a business and web metrics problem than something specific to SEO, but figuring out how you attribute sales to different channels and factoring in life-time value to your site’s traffic can be tricky. Make sure you’re applying the same types of tough questions and attempting to measure SEO the same way you would with any other marketing endeavor. You can learn more about multi-channel attribution in Avinash Kaushik’s in-depth guide
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Some marketers prefer linear models that divide the conversion and revenue evenly across all touch points; others prefer exponential decay models where the lower funnel touches get the lion’s share of credit, but upper funnel interactions still receive some acknowledgment for their role; still others have found great success in machine learning, dynamic attribution models. Avinash Kaushik (Google’s Digital Marketing Evangelist) has a well-rounded article on the topic.
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Then we all realized that this probably doesn’t make a lot of sense when some channels are much more likely to be at the end of the user purchase funnel than others (cue Mr. Kaushik), and we then went to town on creating our own cool attribution models inside Google Analytics and look at assisted revenue instead of Satan’s own offspring, last-click attribution.
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Attribution and life-time value metrics can be tricky. This is more of a business and web metrics problem than something specific to SEO, but figuring out how you attribute sales to different channels and factoring in life-time value to your site’s traffic can be tricky. Make sure you’re applying the same types of tough questions and attempting to measure SEO the same way you would with any other marketing endeavor. You can learn more about multi-channel attribution in Avinash Kaushik’s in-depth guide
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Attribution – the attribution section compares different models in relation to how much a certain channel affected a conversion. Avinash Kaushik’s blog post is a highly informative read on this topic.
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Attribution technology is absolutely overwhelming. Attribution options (last click, first click, Time Delay, game theory and so on) can be overwhelming. But there’s a lot you can accomplish just by setting up conversion pixels and Google Analytics accounts correctly — and if you’re a B2B, making sure your CRM is integrated with your marketing efforts. (There’s a wealth of info out there on attribution, but this post by Google’s Avinash Kaushik is a good starting point.)
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Today, an integrated multi-channel digital marketing approach is needed, along with a better understanding of multi-channel attribution. Too often paid, social and other digital marketing channels are abandoned before they have a chance to show their true value, so hotels need to be sure that they are assessing how each channel plays into assisted conversions and what differences are seen in conversion rates using alternative attribution models first.
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The “Holy Grail” of models is the kind that is based off of your own data, AKA Custom. If you want to try your hand at this model, make meetings with your business leaders to understand historical performance, current marketing mix, and spend patterns. Here are a few questions to get you started:
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New possibilities. I’m most excited about the new possibilities. Here are two… The accelerated death of last-click measurement opens new possibilities about marketing as a portfolio strategy rather than a silo strategy (with huge implications on your team’s org structure).
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Ultimately, marketers desire to apply a percentage value that can be attached to each channel's contribution to the purchasing event (or revenue). This is critical, as it allows the organization to determine how important each channel was in the customer journey, and subsequently, influence how future media spend should be allocated. Sounds fairly easy, right? Well, as Avinash Kaushik (digital analytics thought leader at Google) stated in his influential blog post on multi-channel attribution modeling:
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There is no limit to the number of combinations that can occur, but you’ll likely find that some are more common than others. This data can help you decide where your marketing dollars should go, and how you should approach brand new prospects. Admittedly once you introduce this feature into your company, revenue attribution can become a bit of a headache. However, the value of this data is worth any temporary complexities.
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Open Conversions > Attribution > Model Comparison Tool. The Model Comparison Tool will help you determine the most effective marketing Channels for investment. There is a great post here that explains the different attribution models and their pros and cons. Avinash Kaushik has written an excellent explanation of the model comparison tool here.
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Be careful to understand the difference between first touch, last touch, and various multi-touch attribution models, since different tools will give you different calculations. One of your biggest value adds can be understanding the differences in models, and deciding which model make sense to use or creating your own. For example, where is your team struggling the most? If it’s general awareness, perhaps focus on first touch success. If it’s conversion rates, multi-touch or last touch might be more important.
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Avinash Kaushik wrote extensively on exactly this topic over 2 years ago and not too much has changed since then: most of the commonly used attribution models in Google Analytics(and other comparable analytics suites) are deficient in one way or another and marketing teams should look to customize and hone their attribution models and abandon “out of the box” ones like First Click or Last Click attribution.
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To allow for attribution, we now pull revenue data from Google Analytics, which means we can actually pull data for different attribution models available within Google Analytics. For a starting point on attribution analysis suitability and approach, I recommend Avinash Kaushik’s primer on the topic.
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It seems like attribution has been a problem for marketers for a very long time. According to a popular quote by Avinash Kaushik of Google: “There are few things more complicated in analytics (all analytics, big data and huge data!) than multichannel attribution modeling."
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However, there are others (linear model, last Adwords click, last non-direct click and more), to read about them and learn more about attribution models in general check out one [or both] of the following links: Multi-channel attribution models explained by Avinash Kaushik
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Attribution is, itself, an in-depth post, so, other than asking you to think deeply about Avinash Kaushik’s excellent primer, let’s restrict ourselves to the most pertinent and independently measurable facet of attribution as it relates to SEO year-on-year performance: How much has SERP overlap affected SEO channel traffic capture year-on-year?
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Please start tracking all of your online metrics (and yes – include the miserable number of direct conversions). Now it will be the moment to consider a customised multichannel attribution model because it is highly possible that you will notice traffic or conversions as a result of your awareness efforts. Whether this happens straight away or days later, is also a big factor in the evaluation process.
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Attribution is, itself, an in-depth post, so, other than asking you to think deeply about Avinash Kaushik’s excellent primer, let’s restrict ourselves to the most pertinent and independently measurable facet of attribution as it relates to SEO year-on-year performance: How much has SERP overlap affected SEO channel traffic capture year-on-year?
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It’s important to understand which directories bring new business and which ones act as the last point of touch before a sale/signup is made and act/bid accordingly. Your marketing attribution model matters a lot in B2B. This awesome article by Avinash will get you up to speed. My favorites are Time Decay and Position Based.
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Avinash Kaushik, one of the smartest website analytics minds on the planet, shows you why your website analytics tell lies in this thorough article on the most important concept to understand when measuring website conversions — attribution.
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You can further dissect individual channel contributions to your conversions and sales, inside the Assisted Conversions report. If you’re interested in learning about ROI from individual channels, then Avinash Kaushik talks, in detail, about multi-channel attribution modeling here.
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Unfortunately, getting a PhD might be easier than successfully, accurately setting up full funnel attribution. Exhibit A: this excellent blog post on multi-channel attribution by Avanish. If you can decipher that without popping a few Nuvigil (Limitless) pills, have at it! For the rest of us mere mortals, what to do about it?
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If you’re fortunate enough to work with an analytics team, ask them about attribution modeling and whether they have implemented a model of your organization. If you’re on your own, reference these guides for Adobe Analytics attribution modeling and Google Analytics attribution modeling to dive into the specifics.
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Content is awesome for Brand Awareness but we need to do some Retargeting if we want to increase website conversion rates and improve our results. If you want to learn more about Attribution Models, check out this article by Avinash Kaushik: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models.
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This is why Adwords is mostly an awareness channel and it should be evaluated as such. By awareness I don’t mean impressions or other vanity metrics. What I mean is that it should bring people in, but chances are they won’t convert right there on the spot. They might come back later on and convert. Adwords then should be your first point of contact in your customer’s journey and this should be reflected in your attribution model.
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And I haven’t even mentioned your best bet: the custom option (a model based on your platform, audience, marketing, and specific business goals). Avinash Kaushik has a great walkthrough of setting up your own custom model on Google Analytics over at Occam’s Razor. But be prepared. He begins the post with these ominous words: “There are few things more complicated in analytics (all analytics, big data and huge data!) than multi-channel attribution modeling.”
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The most common place marketers practice attribution is in sources attribution. In this aspect, marketers apply different models to help the better understand the value of different traffic sources in a multi-touch journey, (for a deep dive on attribution models, see this great article by Avinash). Similarly, in event attribution we are also looking to allocate the value between different events in the online journey. Before we address event attribution, we first need to confront a deeper issue which affects all aspects of marketing attribution:
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I am not going to re-invent the wheel about attribution models, Avinash made our day easier about them with this comprehensive article, providing all the necessary information about default attribution models (along with their pros & cons) and his preferred custom attribution model as well. I just want to share a different point of view based on personal learnings about marketing attribution models.
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Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models – The great Avinash Kaushik’s introduction to attribution modeling. There is a lot more detail in this post than our and a great next step. While you’re there check out the rest of his attribution posts – you’ll thank us later.
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To account for these optimistic numbers, I recommend using position-based models for ecommerce that give credit to first, middle and last interactions. The exact settings should incorporate industry averages and user behavior, namely the number of touches it typically takes for a site visitor to become a customer. A helpful resource I’ve revisited when customizing attribution models for clients is “Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models” by Avinash Kaushik, Google’s Digital Marketing Evangelist.
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Set goals in Master View. Understand goals, review the ones that you have or create new. You have to have goals to measure your site performance, to track conversions. Check your attribution model to ensure that it matches your visitors’ behavior. Google provides you with default models. Understand attribution modeling and adjust the settings to fit your site and audience.
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Marketers also have access to other attribution models within certain analytics platforms. These include time decay, customizable, linear, and position-based. There are pros and cons to all of these. Google Attribution uses machine learning to understand your conversion paths and the sequential order for each touchpoint that a prospect hits before becoming a customer. This analysis will give you an attribution model that is custom to your business. This is the golden ticket for marketers.
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This is the “Holy Grail” of attribution models! You can mold your model around more specific business questions and objectives and compare your custom model and other default models side-by-side. Here is an example of how one would look for Rockstar:
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Despite the insightful advice of writers like Avinash Kaushik, users of online attribution models still frequently default to flawed methods like last-click attribution. In addition, repeated waves of announcements about inaccurate data have sometimes created an atmosphere of mistrust and questions about transparency, made even worse by the existence of click fraud, especially for desktop video ads.
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The brilliant Avinash Kaushik has detailed attribution problems in depth. The crux of the attribution problem is that no decision is linear. Even something as small as choosing what to eat for breakfast each morning has more inputs than we could acknowledge in the next 10 minutes. What did our parents feed us growing up? What did we have a bad reaction to?
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The result is that you can’t tell what came before it. Which means that you typically won’t give those other efforts the credit they deserve. Read: more budget, people, or time. Your best bet here is to use a multi-step process instead of last-touch only.
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If you want to learn more on how to use Multi-Channel Attribution Modeling, I would suggest reading Avinash Kaushik’s Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models. The post is several years old and some of the screen shots for Google Analytics are dated, but the logic and concepts are still valid.
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The ‘last-touch bias’ might tell you that direct referrals drive your sales. But clearly, that’s not the whole story. Instead, you need to switch your attribution model to a multi-step process. In the conversion reports section, you can analyze conversion paths and assisted conversions. Here are a few of your options.
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Google Analytics gives you a new perspective on your landing pages, which is useful for getting the most out of your ads in AdWords. Observe the behavior of the ad traffic from the landing page onwards as they journey through your website. You can also monitor whether the visitors return another time through multi-channel attribution.
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Recently, It got me to thinking what matter’s the most the first attribution Or the last attribution? Well, it depends… The first attribution might not be relevant. Avinash Kaushik in his article here quotes,
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While Google Analytics isn’t great for tracking offline events like sales conversations, it can be great for tracking contribution marketing. You just need to look at your multi-step attribution model.
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It’s time to get started with marketing attribution. Below, you’ll learn the seven attribution models you can use to get deeper performance insights across your marketing campaigns, optimize your budget, and get rid of the anchors. (Source)
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However, it is not possible to use it without some modifications. Check out the series of posts on this on Avinash’s blog before setting out to develop your own attribution model. Setting this up is not easy and requires a long-term commitment. However, once you manage this, you will be able to waste less money on ineffective marketing channels and use the most profitable channels to bring the visitors to your site who are more likely to convert.
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